five modes duplication

2023-03-16 22:42:35 +08:00
commit a9f5451c36
34 changed files with 28042 additions and 0 deletions
--- a/.DS_Store
+++ b/.DS_Store
--- a/20230309_fxy_psiphon_operation.pcapng
+++ b/20230309_fxy_psiphon_operation.pcapng
--- a/C4.5_test.py
+++ b/C4.5_test.py
@@ -0,0 +1,47 @@
+# Name:fang xiaoyu
+# Time: 2023/3/11 22:28
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.metrics import accuracy_score
+from sklearn import preprocessing
+from sklearn import tree
+from sklearn.metrics import classification_report
+
+# 加载CSV文件
+data = pd.read_csv('sufshark_openvpn_tcp+youdao_header.csv')
+
+# 将类别转换为数字标签
+# le = preprocessing.LabelEncoder()
+# data['label'] = le.fit_transform(data['label'])
+data["class1"] = data["class1"].replace({"VPN": 1, "Non-VPN": 0})
+
+# 分离特征和类别
+X = data.iloc[:, :-1]
+y = data.iloc[:, -1]
+
+# 划分数据集
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
+
+# 初始化分类器并训练模型
+clf = DecisionTreeClassifier(criterion="entropy")
+clf.fit(X_train, y_train)
+
+# 预测测试数据集
+y_pred = clf.predict(X_test)
+
+# 评估分类器的性能
+print("Accuracy:", accuracy_score(y_test, y_pred))
+print(classification_report(y_test, y_pred))
+# 可视化决策树
+tree.plot_tree(clf)
+
+#Accuracy: 0.8841506751954513
+#               precision    recall  f1-score   support
+#
+#            0       0.89      0.86      0.87      2708
+#            1       0.88      0.90      0.89      2920
+#
+#     accuracy                           0.88      5628
+#    macro avg       0.88      0.88      0.88      5628
+# weighted avg       0.88      0.88      0.88      5628
--- a/Knn_test.py
+++ b/Knn_test.py
@@ -0,0 +1,33 @@
+# Name:fang xiaoyu
+# Time: 2023/3/11 22:05
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.metrics import accuracy_score
+from sklearn.metrics import classification_report
+
+data = pd.read_csv('sufshark_openvpn_tcp+youdao_header.csv')
+
+
+data["class1"] = data["class1"].replace({"VPN": 1, "Non-VPN": 0})
+
+#print(data)
+X_train, X_test, y_train, y_test = train_test_split(
+    data.iloc[:, :-1], data.iloc[:, -1], test_size=0.2, random_state=42)
+
+knn = KNeighborsClassifier(n_neighbors=3)
+knn.fit(X_train, y_train)
+
+y_pred = knn.predict(X_test)
+accuracy = accuracy_score(y_test, y_pred)
+print(f"Accuracy: {accuracy}")
+print(classification_report(y_test, y_pred))
+#Accuracy: 0.8200959488272921
+#               precision    recall  f1-score   support
+#
+#            0       0.79      0.83      0.81      1767
+#            1       0.84      0.81      0.83      1985
+#
+#     accuracy                           0.82      3752
+#    macro avg       0.82      0.82      0.82      3752
+# weighted avg       0.82      0.82      0.82      3752
--- a/Randomforest_test.py
+++ b/Randomforest_test.py
@@ -0,0 +1,55 @@
+# Name:fang xiaoyu
+# Time: 2023/3/11 23:18
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score, confusion_matrix , precision_score, recall_score, f1_score
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import classification_report
+# 读取数据
+data = pd.read_csv('sufshark_openvpn_tcp+youdao_header.csv')
+
+# 将类别转换为数字标签
+data["class1"] = data["class1"].replace({"VPN": 1, "Non-VPN": 0})
+
+# 提取特征和标签
+X = data.drop('class1', axis=1)
+y = data['class1']
+
+
+# 划分训练集和测试集
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
+
+# 创建随机森林分类器
+rfc = RandomForestClassifier(n_estimators=100, criterion='gini', max_depth=10, min_samples_split=2, min_samples_leaf=1,
+                              max_features='auto', bootstrap=True, random_state=42)
+
+# 训练模型
+rfc.fit(X_train, y_train)
+
+# 预测测试集
+y_pred = rfc.predict(X_test)
+
+# 计算准确率
+accuracy = accuracy_score(y_test, y_pred)
+precision = precision_score(y_test,y_pred)
+recall = recall_score(y_test,y_pred)
+f1 = f1_score(y_test,y_pred)
+
+print('Accuracy:', accuracy)
+print("Precision:",precision)
+print("Recall:",recall)
+print("f1_score:",f1)
+print(classification_report(y_test, y_pred))
+# Accuracy: 0.8909026297085999
+# Precision: 0.8626424391746227
+# Recall: 0.9434152913438868
+# f1_score: 0.9012226512226512
+
+# 混淆矩阵可视化
+conf_mat = confusion_matrix(y_test, y_pred)
+sns.heatmap(conf_mat, annot=True, cmap='Blues', fmt='g')
+plt.xlabel('Predicted')
+plt.ylabel('Actual')
+plt.show()
--- a/SVM_test.py
+++ b/SVM_test.py
@@ -0,0 +1,44 @@
+# Name:fang xiaoyu
+# Time: 2023/3/11 22:30
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.svm import SVC
+from sklearn.metrics import classification_report
+
+# 读取CSV文件
+data = pd.read_csv('sufshark_openvpn_tcp+youdao_header.csv')
+
+# 将类别转换为数字标签
+# le = preprocessing.LabelEncoder()
+# data['label'] = le.fit_transform(data['label'])
+data["class1"] = data["class1"].replace({"VPN": 1, "Non-VPN": 0})
+
+# 分离特征和类别
+X = data.iloc[:, :-1]
+y = data.iloc[:, -1]
+
+# # 分离特征和标签
+# X = data.drop('label', axis=1)
+# y = data['label']
+
+# 划分训练集和测试集
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
+
+# 创建SVM模型
+svm_model = SVC()
+
+# 在训练集上训练模型
+svm_model.fit(X_train, y_train)
+
+# 在测试集上评估模型
+predictions = svm_model.predict(X_test)
+print(classification_report(y_test, predictions))
+
+#      precision    recall  f1-score   support
+#
+#            0       0.59      0.42      0.49      1720
+#            1       0.61      0.76      0.67      2032
+#
+#     accuracy                           0.60      3752
+#    macro avg       0.60      0.59      0.58      3752
+# weighted avg       0.60      0.60      0.59      3752
--- a/ScenarioA.pkl
+++ b/ScenarioA.pkl
--- a/TimeBasedFeatures-Dataset-15s-VPN.csv
+++ b/TimeBasedFeatures-Dataset-15s-VPN.csv
--- a/XGBoost_test.py
+++ b/XGBoost_test.py
@@ -0,0 +1,68 @@
+# Name:fang xiaoyu
+# Time: 2023/3/11 22:35
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score, confusion_matrix , precision_score, recall_score, f1_score
+import xgboost as xgb
+from sklearn.metrics import classification_report
+
+# 读取CSV文件
+df = pd.read_csv('sufshark_openvpn_tcp+youdao_header.csv')
+
+
+# 将VPN和非VPN数据分为两个类别
+df['class1'] = np.where(df['class1'] == 'VPN', 1, 0)
+
+
+# 划分训练集和测试集
+train, test = train_test_split(df, test_size=0.2, random_state=42)
+
+# 将数据转换为DMatrix格式
+train_dmatrix = xgb.DMatrix(data=train.drop(['class1'], axis=1), label=train['class1'])
+test_dmatrix = xgb.DMatrix(data=test.drop(['class1'], axis=1), label=test['class1'])
+
+# print(train_dmatrix)
+# print(test_dmatrix)
+
+# 定义XGBoost模型参数
+params = {
+    'objective': 'binary:logistic',
+    'eval_metric': 'auc',
+    'eta': 0.1,
+    'max_depth': 6,
+    'min_child_weight': 1,
+    'subsample': 0.8,
+    'colsample_bytree': 0.8,
+    'seed': 42
+}
+
+# 训练XGBoost模型
+xgb_model = xgb.train(
+    params=params,
+    dtrain=train_dmatrix,
+    num_boost_round=100,
+    early_stopping_rounds=10,
+    evals=[(test_dmatrix, 'test')]
+)
+
+# 对测试集进行预测
+y_pred = xgb_model.predict(test_dmatrix)
+
+# 将预测结果转换为类别标签
+y_pred_label = np.where(y_pred > 0.5, 1, 0)
+
+# 计算模型准确性
+accuracy = accuracy_score(test['class1'], y_pred_label)
+precision = precision_score(test['class1'], y_pred_label)
+recall = recall_score(test['class1'], y_pred_label)
+f1 = f1_score(test['class1'], y_pred_label)
+print(f"Accuracy: {accuracy}")
+print(f"Precision: {precision}")
+print(f"Recall: {recall}")
+print(f"F1-score: {f1}")
+print(classification_report(test['class1'], y_pred_label))
+# Accuracy: 0.9139125799573561
+# Precision: 0.8972275334608031
+# Recall: 0.9455919395465995
+# F1-score: 0.9207750797154771
--- a/example.py
+++ b/example.py
@@ -0,0 +1,308 @@
+# Name:fang xiaoyu
+# Time: 2023/3/9 23:40
+
+#用Python写一个使用trannalyzer2来提取网络流量特征，并使用这些特征来训练一个KNN分类模型
+'''
+以下是一个简单的Python代码示例，用于使用Tranalyzer2提取网络流量特征，并使用这些特征来训练一个KNN分类模型：
+
+```python
+import os
+import json
+import pandas as pd
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.model_selection import train_test_split
+
+# 定义Tranalyzer2命令和特征提取命令
+tranalyzer_cmd = "tranalyzer2 -r {} -w {} -t"
+feature_cmd = "tranalyzer2 -r {} --bidir --tcp --protoid --statsonly --export json"
+
+# 定义pcap文件路径和输出文件路径
+pcap_file = "capture.pcap"
+binetflow_file = "capture.binetflow"
+
+# 转换pcap文件为binetflow格式
+os.system(tranalyzer_cmd.format(pcap_file, binetflow_file))
+
+# 提取特征并保存到json文件中
+os.system(feature_cmd.format(binetflow_file) + " > features.json")
+
+# 读取json文件中的特征数据并转换为DataFrame格式
+with open("features.json", "r") as f:
+    data = json.load(f)
+df = pd.DataFrame(data)
+
+# 将标签列转换为数值类型（0或1）
+df["label"] = df["label"].apply(lambda x: 0 if x == "normal" else 1)
+
+# 将数据集划分为训练集和测试集
+X_train, X_test, y_train, y_test = train_test_split(df.drop("label", axis=1), df["label"], test_size=0.2)
+
+# 创建KNN分类器对象，设置邻居数量为5
+knn_model = KNeighborsClassifier(n_neighbors=5)
+
+# 训练模型并预测测试集结果
+knn_model.fit(X_train, y_train)
+y_pred = knn_model.predict(X_test)
+
+# 输出准确率和混淆矩阵等评估指标
+from sklearn.metrics import accuracy_score, confusion_matrix
+
+print("Accuracy:", accuracy_score(y_test, y_pred))
+print("Confusion Matrix:\n", confusion_matrix(y_test, y_pred))
+```
+
+在这个示例中，我们首先定义了Tranalyzer2的命令和特征提取命令，并指定了输入pcap文件和输出binetflow文件的路径。然后，我们使用os库调用系统命令来执行Tranalyzer2转换操作，并将特征保存到json格式的文件中。
+
+接下来，我们使用pandas库将json文件中的特征数据读取为DataFrame格式，并将标签列转换为数值类型（0或1）。然后，我们使用train_test_split函数将数据集划分为训练集和测试集。
+
+接着，我们创建了一个KNN分类器对象，并设置邻居数量为5。然后，我们使用fit方法训练模型，并使用predict方法预测测试集结果。
+
+最后，我们输出了准确率和混淆矩阵等评估指标。需要注意的是，在实际应用中，需要根据具体需求选择不同
+'''
+
+
+
+#用Python写一个使用trannalyzer2来提取网络流量特征，并使用这些特征来训练一个SVm分类模型
+'''
+以下是一个简单的Python代码示例，用于使用Tranalyzer2提取网络流量特征，并使用这些特征来训练一个SVM分类模型：
+
+```python
+import os
+import json
+import pandas as pd
+from sklearn.svm import SVC
+from sklearn.model_selection import train_test_split
+
+# 定义Tranalyzer2命令和特征提取命令
+tranalyzer_cmd = "tranalyzer2 -r {} -w {} -t"
+feature_cmd = "tranalyzer2 -r {} --bidir --tcp --protoid --statsonly --export json"
+
+# 定义pcap文件路径和输出文件路径
+pcap_file = "capture.pcap"
+binetflow_file = "capture.binetflow"
+
+# 转换pcap文件为binetflow格式
+os.system(tranalyzer_cmd.format(pcap_file, binetflow_file))
+
+# 提取特征并保存到json文件中
+os.system(feature_cmd.format(binetflow_file) + " > features.json")
+
+# 读取json文件中的特征数据并转换为DataFrame格式
+with open("features.json", "r") as f:
+    data = json.load(f)
+df = pd.DataFrame(data)
+
+# 将标签列转换为数值类型（0或1）
+df["label"] = df["label"].apply(lambda x: 0 if x == "normal" else 1)
+
+# 将数据集划分为训练集和测试集
+X_train, X_test, y_train, y_test = train_test_split(df.drop("label", axis=1), df["label"], test_size=0.2)
+
+# 创建SVM分类器对象，设置核函数为rbf，惩罚参数为1.0，gamma参数为scale
+svm_model = SVC(kernel="rbf", C=1.0, gamma="scale")
+
+# 训练模型并预测测试集结果
+svm_model.fit(X_train, y_train)
+y_pred = svm_model.predict(X_test)
+
+# 输出准确率和混淆矩阵等评估指标
+from sklearn.metrics import accuracy_score, confusion_matrix
+
+print("Accuracy:", accuracy_score(y_test, y_pred))
+print("Confusion Matrix:\n", confusion_matrix(y_test, y_pred))
+```
+'''
+'''
+在这个示例中，我们首先定义了Tranalyzer2的命令和特征提取命令，并指定了输入pcap文件和输出binetflow文件的路径。然后，我们使用os库调用系统命令来执行Tranalyzer2转换操作，并将特征保存到json格式的文件中。
+
+接下来，我们使用pandas库将json文件中的特征数据读取为DataFrame格式，并将标签列转换为数值类型（0或1）。然后，我们使用train_test_split函数将数据集划分为训练集和测试集。
+
+接着，我们创建了一个SVM分类器对象，并设置核函数为rbf，惩罚参数为1.0，gamma参数为scale。然后，我们使用fit方法训练模型，并使用predict方法预测测试集结果。
+
+最后，我们输出了准确率和混淆矩阵等评估指标。需要注意的是，在实际应用中，需要根据具体需求选择不同的特征提取选项和分类器模型，并对模型进行调参等操作以提高性能。
+'''
+
+
+#用Python写一个使用trannalyzer2来提取网络流量特征，并使用这些特征来训练一个C4.5分类模型
+"""
+以下是一个简单的Python代码示例，用于使用Tranalyzer2提取网络流量特征，并使用这些特征来训练一个C4.5分类模型：
+
+```python
+import os
+import json
+import pandas as pd
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.model_selection import train_test_split
+
+# 定义Tranalyzer2命令和特征提取命令
+tranalyzer_cmd = "tranalyzer2 -r {} -w {} -t"
+feature_cmd = "tranalyzer2 -r {} --bidir --tcp --protoid --statsonly --export json"
+
+# 定义pcap文件路径和输出文件路径
+pcap_file = "capture.pcap"
+binetflow_file = "capture.binetflow"
+
+# 转换pcap文件为binetflow格式
+os.system(tranalyzer_cmd.format(pcap_file, binetflow_file))
+
+# 提取特征并保存到json文件中
+os.system(feature_cmd.format(binetflow_file) + " > features.json")
+
+# 读取json文件中的特征数据并转换为DataFrame格式
+with open("features.json", "r") as f:
+    data = json.load(f)
+df = pd.DataFrame(data)
+
+# 将标签列转换为数值类型（0或1）
+df["label"] = df["label"].apply(lambda x: 0 if x == "normal" else 1)
+
+# 将数据集划分为训练集和测试集
+X_train, X_test, y_train, y_test = train_test_split(df.drop("label", axis=1), df["label"], test_size=0.2)
+
+# 创建C4.5分类器对象，设置最大深度为3，信息增益阈值为0.1，最小样本数为10个样本点。
+c45_model = DecisionTreeClassifier(criterion="entropy", max_depth=3, min_samples_leaf=10)
+
+# 训练模型并预测测试集结果
+c45_model.fit(X_train, y_train)
+y_pred = c45_model.predict(X_test)
+
+# 输出准确率和混淆矩阵等评估指标
+from sklearn.metrics import accuracy_score, confusion_matrix
+
+print("Accuracy:",accuracy_score(y_test, y_pred))
+print("Confusion Matrix:\n", confusion_matrix(y_test, y_pred))
+```
+
+在这个示例中，我们首先定义了Tranalyzer2的命令和特征提取命令，并指定了输入pcap文件和输出binetflow文件的路径。然后，我们使用os库调用系统命令来执行Tranalyzer2转换操作，并将特征保存到json格式的文件中。
+
+接下来，我们使用pandas库将json文件中的特征数据读取为DataFrame格式，并将标签列转换为数值类型（0或1）。然后，我们使用train_test_split函数将数据集划分为训练集和测试集。
+
+接着，我们创建了一个C4.5分类器对象，并设置最大深度为3，信息增益阈值为0.1，最小样本数为10个样本点。然后，我们使用fit方法训练模型，并使用predict方法预测测试集结果。
+
+最后，我们输出了准确率和混淆矩阵等评估指标。需要注意的是，在实际应用中，需要根据具体需求选择不同的特征提取选项和分类器模型，并对模型进行调参等操作以提高性能。
+
+"""
+
+#
+'''
+以下是一个简单的Python代码示例，用于使用Tranalyzer2提取网络流量特征，并使用这些特征来训练一个Random Forest分类模型：
+
+```python
+import os
+import json
+import pandas as pd
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import train_test_split
+
+# 定义Tranalyzer2命令和特征提取命令
+tranalyzer_cmd = "tranalyzer2 -r {} -w {} -t"
+feature_cmd = "tranalyzer2 -r {} --bidir --tcp --protoid --statsonly --export json"
+
+# 定义pcap文件路径和输出文件路径
+pcap_file = "capture.pcap"
+binetflow_file = "capture.binetflow"
+
+# 转换pcap文件为binetflow格式
+os.system(tranalyzer_cmd.format(pcap_file, binetflow_file))
+
+# 提取特征并保存到json文件中
+os.system(feature_cmd.format(binetflow_file) + " > features.json")
+
+# 读取json文件中的特征数据并转换为DataFrame格式
+with open("features.json", "r") as f:
+    data = json.load(f)
+df = pd.DataFrame(data)
+
+# 将标签列转换为数值类型（0或1）
+df["label"] = df["label"].apply(lambda x: 0 if x == "normal" else 1)
+
+# 将数据集划分为训练集和测试集
+X_train, X_test, y_train, y_test = train_test_split(df.drop("label", axis=1), df["label"], test_size=0.2)
+
+# 创建Random Forest分类器对象，设置树的数量为100，最大深度为10，信息增益阈值为0.1。
+rf_model = RandomForestClassifier(n_estimators=100, max_depth=10, criterion="entropy", min_samples_leaf=10)
+
+# 训练模型并预测测试集结果
+rf_model.fit(X_train, y_train)
+y_pred = rf_model.predict(X_test)
+
+# 输出准确率和混淆矩阵等评估指标
+from sklearn.metrics import accuracy_score, confusion_matrix
+
+print("Accuracy:", accuracy_score(y_test, y_pred))
+print("Confusion Matrix:\n", confusion_matrix(y_test, y_pred))
+```
+'''
+'''
+在这个示例中，我们首先定义了Tranalyzer2的命令和特征提取命令，并指定了输入pcap文件和输出binetflow文件的路径。然后，我们使用os库调用系统命令来执行Tranalyzer2转换操作，并将特征保存到json格式的文件中。
+
+接下来，我们使用pandas库将json文件中的特征数据读取为DataFrame格式，并将标签列转换为数值类型（0或1）。然后，我们使用train_test_split函数将数据集划分为训练集和测试集。
+
+接着，我们创建了一个Random Forest分类器对象，并设置树的数量为100，最大深度为10，信息增益阈值为0.1。然后，我们使用fit方法训练模型，并使用predict方法预测测试集结果。
+
+最后，我们输出了准确率和混淆矩阵等评估指标。需要注意的是，在实际应用中，需要根据具体需求选择不同的特征提取选项和分类器模型，并对模型进行调参等操作以提高性能。
+'''
+
+
+
+
+#以下是一个简单的Python代码示例，用于使用Tranalyzer2提取网络流量特征，并使用这些特征来训练一个XGBoost分类模型：
+
+'''
+```python
+import os
+import json
+import pandas as pd
+import xgboost as xgb
+from sklearn.model_selection import train_test_split
+
+# 定义Tranalyzer2命令和特征提取命令
+tranalyzer_cmd = "tranalyzer2 -r {} -w {} -t"
+feature_cmd = "tranalyzer2 -r {} --bidir --tcp --protoid --statsonly --export json"
+
+# 定义pcap文件路径和输出文件路径
+pcap_file = "capture.pcap"
+binetflow_file = "capture.binetflow"
+
+# 转换pcap文件为binetflow格式
+os.system(tranalyzer_cmd.format(pcap_file, binetflow_file))
+
+# 提取特征并保存到json文件中
+os.system(feature_cmd.format(binetflow_file) + " > features.json")
+
+# 读取json文件中的特征数据并转换为DataFrame格式
+with open("features.json", "r") as f:
+    data = json.load(f)
+df = pd.DataFrame(data)
+
+# 将标签列转换为数值类型（0或1）
+df["label"] = df["label"].apply(lambda x: 0 if x == "normal" else 1)
+
+# 将数据集划分为训练集和测试集
+X_train, X_test, y_train, y_test = train_test_split(df.drop("label", axis=1), df["label"], test_size=0.2)
+
+# 创建XGBoost分类器对象，设置树的数量为100，最大深度为10，学习率为0.1。
+xgb_model = xgb.XGBClassifier(n_estimators=100, max_depth=10, learning_rate=0.1)
+
+# 训练模型并预测测试集结果
+xgb_model.fit(X_train, y_train)
+y_pred = xgb_model.predict(X_test)
+
+# 输出准确率和混淆矩阵等评估指标
+from sklearn.metrics import accuracy_score, confusion_matrix
+
+print("Accuracy:", accuracy_score(y_test, y_pred
+继续
+))
+print("Confusion Matrix:\n", confusion_matrix(y_test, y_pred))
+```
+'''
+'''
+在这个示例中，我们首先定义了Tranalyzer2的命令和特征提取命令，并指定了输入pcap文件和输出binetflow文件的路径。然后，我们使用os库调用系统命令来执行Tranalyzer2转换操作，并将特征保存到json格式的文件中。
+
+接下来，我们使用pandas库将json文件中的特征数据读取为DataFrame格式，并将标签列转换为数值类型（0或1）。然后，我们使用train_test_split函数将数据集划分为训练集和测试集。
+
+接着，我们创建了一个XGBoost分类器对象，并设置树的数量为100，最大深度为10，学习率为0.1。然后，我们使用fit方法训练模型，并使用predict方法预测测试集结果。
+
+最后，我们输出了准确率和混淆矩阵等评估指标。需要注意的是，在实际应用中，需要根据具体需求选择不同的特征提取选项和分类器模型，并对模型进行调参等操作以提高性能。
+'''
--- a/features.json
+++ b/features.json
--- a/merge_pcap.py
+++ b/merge_pcap.py
@@ -0,0 +1,32 @@
+# Name:fang xiaoyu
+# Time: 2023/3/11 11:40
+import os
+import dpkt
+import pcapy
+
+# 指定要合并的文件夹路径和合并后的文件名
+folder_path = "/path/to/folder"
+merged_file = "merged.pcap"
+
+# 获取文件夹内所有的pcap文件
+pcap_files = [f for f in os.listdir(folder_path) if f.endswith('.pcap')]
+
+# 打开第一个pcap文件，读取第一个数据包的时间戳
+reader = pcapy.open_offline(os.path.join(folder_path, pcap_files[0]))
+pcap = dpkt.pcap.Reader(reader)
+_, ts = next(pcap)
+
+# 创建一个新的pcap文件并写入第一个数据包
+writer = dpkt.pcap.Writer(open(merged_file, 'wb'))
+writer.writepkt(_, ts)
+
+# 依次读取每个pcap文件的数据包并写入到新的pcap文件中
+for pcap_file in pcap_files:
+    reader = pcapy.open_offline(os.path.join(folder_path, pcap_file))
+    pcap = dpkt.pcap.Reader(reader)
+    for ts, buf in pcap:
+        writer.writepkt(buf, ts)
+
+# 关闭文件句柄
+writer.close()
+
--- a/output.csv
+++ b/output.csv
@@ -0,0 +1 @@
+src_ip,dst_ip,src_port,dst_port,proto,num_packets,bytes,duration,timestamp_start,timestamp_end,flags
--- a/sufshark_openvpn_tcp+youdao_header.csv
+++ b/sufshark_openvpn_tcp+youdao_header.csv
--- a/test.py
+++ b/test.py
@@ -0,0 +1,19 @@
+# Name:fang xiaoyu
+# Time: 2023/3/9 22:07
+
+import pickle
+from utils.utils import *
+
+path = "ScenarioA.pkl"
+
+f = open(path , 'rb')
+data = pickle.load(f)
+
+print(len(data))
+
+# import pickle as pkl
+# f = open('ScenarioA.pkl', 'rb')
+# b = pkl.load(f)
+# b = list(b)
+# b = np.array(b)
+# print(b)
--- a/test2.py
+++ b/test2.py
@@ -0,0 +1,48 @@
+# Name:fang xiaoyu
+# Time: 2023/3/10 09:17
+import os
+import json
+import pandas as pd
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.model_selection import train_test_split
+
+# 定义Tranalyzer2命令和特征提取命令
+tranalyzer_cmd = "t2 -r {} -w {} -t"
+feature_cmd = "t2 -r {} --bidir --tcp --protoid --statsonly --export json"
+
+# 定义pcap文件路径和输出文件路径
+pcap_file = "20230309_fxy_psiphon_operation.pcapng"
+binetflow_file = "capture.binetflow"
+
+# 转换pcap文件为binetflow格式
+os.system(tranalyzer_cmd.format(pcap_file, binetflow_file))
+
+# 提取特征并保存到json文件中
+os.system(feature_cmd.format(binetflow_file) + " > features.json")
+
+# 读取json文件中的特征数据并转换为DataFrame格式
+with open("features.json", "r") as f:
+    data = json.load(f)
+df = pd.DataFrame(data)
+
+# 将标签列转换为数值类型（0或1）
+df["label"] = df["label"].apply(lambda x: 0 if x == "normal" else 1)
+
+# 将数据集划分为训练集和测试集
+X_train, X_test, y_train, y_test = train_test_split(df.drop("label", axis=1), df["label"], test_size=0.2)
+
+# 创建KNN分类器对象，设置邻居数量为5
+knn_model = KNeighborsClassifier(n_neighbors=5)
+
+# 训练模型并预测测试集结果
+knn_model.fit(X_train, y_train)
+y_pred = knn_model.predict(X_test)
+
+# 输出准确率和混淆矩阵等评估指标
+from sklearn.metrics import accuracy_score, confusion_matrix
+
+print("Accuracy:", accuracy_score(y_test, y_pred))
+print("Confusion Matrix:\n", confusion_matrix(y_test, y_pred))
+
+
+#$ tranalyzer2 -r sample.flow -w sample.features -t templates/plugins/ipfix-allfields.txt
--- a/test4.py
+++ b/test4.py
@@ -0,0 +1,91 @@
+# Name:fang xiaoyu
+# Time: 2023/3/11 18:43
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import dpkt
+import socket
+import struct
+import binascii
+from scapy.all import *
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.model_selection import cross_val_score
+#import tshark
+import scapy
+
+# 从pcap文件中读取流量数据
+def read_pcap(filename):
+    packets = rdpcap(filename)
+    flows = {}
+    for packet in packets:
+        if packet.haslayer(TCP):
+            src_ip = packet[IP].src
+            dst_ip = packet[IP].dst
+            src_port = packet[TCP].sport
+            dst_port = packet[TCP].dport
+            key = (src_ip, dst_ip, src_port, dst_port)
+            if key not in flows:
+                flows[key] = [packet]
+            else:
+                flows[key].append(packet)
+    return flows
+
+# 提取流量数据的特征
+def extract_features(flow):
+    features = []
+    total_len = 0
+    total_pkts = len(flow)
+    start_time = flow[0].time
+    end_time = flow[-1].time
+    for packet in flow:
+        total_len += len(packet)
+    duration = end_time - start_time
+    features.append(total_len)
+    features.append(total_pkts)
+    features.append(duration)
+    return features
+
+# 将特征向量转换为numpy数组
+def vectorize_data(data):
+    return np.array(data)
+
+# 读取VPN和non-VPN流量数据
+vpn_traffic = read_pcap('vpn_traffic.pcap')
+nonvpn_traffic = read_pcap('nonvpn_traffic.pcap')
+
+# 提取VPN和non-VPN流量的特征
+vpn_traffic = [extract_features(flow) for flow in vpn_traffic.values()]
+nonvpn_traffic = [extract_features(flow) for flow in nonvpn_traffic.values()]
+
+# 将VPN和non-VPN流量数据转换为numpy数组
+vpn_traffic = vectorize_data(vpn_traffic)
+nonvpn_traffic = vectorize_data(nonvpn_traffic)
+
+# 将VPN和non-VPN流量数据合并
+X = np.concatenate((vpn_traffic, nonvpn_traffic))
+y = np.concatenate((np.ones(len(vpn_traffic)), np.zeros(len(nonvpn_traffic))))
+
+# 使用交叉验证选择最佳的K值
+cv_scores = []
+for k in range(1, 31):
+    knn = KNeighborsClassifier(n_neighbors=k)
+    scores = cross_val_score(knn, X, y, cv=5, scoring='accuracy')
+    cv_scores.append(scores.mean())
+
+# 可视化交叉验证结果
+plt.plot(range(1, 31), cv_scores)
+plt.xlabel('K')
+plt.ylabel('Accuracy')
+plt.show()
+
+# 使用最佳的K值进行模型训练和预测
+best_k = np.argmax(cv_scores) + 1
+knn = KNeighborsClassifier(n_neighbors=best_k)
+knn.fit(X, y)
+
+# 对新数据进行预测
+new_data = read_pcap('new_traffic.pcap')
+new_data = [extract_features(flow) for flow in new_data.values()]
+new_data = vectorize_data(new_data)
+prediction = knn.predict(new_data)
+print(prediction)
--- a/test_2.py
+++ b/test_2.py
@@ -0,0 +1,167 @@
+# # Name:fang xiaoyu
+# # Time: 2023/3/10 23:53
+# # 导入所需库
+# import pandas as pd
+# from sklearn.neighbors import KNeighborsClassifier
+# from sklearn.model_selection import train_test_split
+# from subprocess import run
+#
+# # 使用Tranalyzer2分析PCAP文件，并提取TCP流量特征
+# def extract_features(pcap_file):
+#     # 定义Tranalyzer2命令行参数
+#     tranalyzer_args = [
+#         "t2build", "-x", "tcp", "--no-tests", "--no-progress",
+#         "--tcp-fields", "sip dip sport dport tcp_flags tcp_flags_str bytes",
+#         "--histo", "sip dip sport dport", "--top", "sip dip sport dport bytes",
+#         "--both-ways", "--export", "csv", "-w", "-"
+#     ]
+#     # 运行Tranalyzer2命令行，并将结果保存为CSV文件
+#     result = run(["sudo", "tshark", "-r", pcap_file, "-w", "-", "-F", "pcapng", "-Y", "tcp"],
+#                  stdout=PIPE, check=True)
+#     result = run(["sudo", "tranalyzer2", "-r", "-", *tranalyzer_args], input=result.stdout, stdout=PIPE, check=True)
+#     result_csv = result.stdout.decode()
+#     # 解析CSV文件，并返回特征数据框
+#     features_df = pd.read_csv(pd.compat.StringIO(result_csv))
+#     return features_df
+#
+# # 加载训练数据集，并提取特征
+# train_pcap = "20230309_fxy_psiphon_operation.pcapng"
+# train_labels = pd.read_csv("/path/to/train_labels.csv")
+# train_features = extract_features(train_pcap)
+#
+# # 将标签与特征合并为单个数据框
+# train_data = pd.merge(train_features, train_labels, on="flow_key")
+#
+# # 分割数据集为训练集和测试集
+# X_train, X_test, y_train, y_test = train_test_split(train_data.drop(["flow_key", "label"], axis=1), train_data["label"], test_size=0.3)
+#
+# # 训练KNN模型
+# knn = KNeighborsClassifier(n_neighbors=5)
+# knn.fit(X_train, y_train)
+#
+# # 在测试集上评估模型性能
+# accuracy = knn.score(X_test, y_test)
+# print("Accuracy:", accuracy)
+#
+#
+
+'''
+import subprocess
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import LabelEncoder
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.metrics import accuracy_score
+
+# Step 1: Install Tranalyzer2 and required Python modules
+
+# Step 2: Extract features using Tranalyzer2
+pcap_file = '20230309_fxy_psiphon_operation.pcapng'
+output_file = 'output.csv'
+command = f'sudo t2 -r {pcap_file} -w {output_file} -c basic'
+subprocess.call(command, shell=True)
+
+# Step 3: Load features into a Pandas dataframe and convert to NumPy array
+data = pd.read_csv(output_file)
+features = np.array(data)
+
+# Step 4: Prepare the dataset
+X = features[:, :-1]
+y = features[:, -1]
+
+le = LabelEncoder()
+y = le.fit_transform(y)
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
+
+# Step 5: Train the KNN model
+knn = KNeighborsClassifier(n_neighbors=5)
+knn.fit(X_train, y_train)
+
+# Step 6: Evaluate the model
+y_pred = knn.predict(X_test)
+accuracy = accuracy_score(y_test, y_pred)
+
+print("Accuracy:", accuracy)
+
+'''
+
+import pandas as pd
+import numpy as np
+import os
+import glob
+import subprocess
+from sklearn.model_selection import cross_val_score, train_test_split
+from sklearn.svm import SVC
+from sklearn.metrics import accuracy_score
+
+
+# 提取流量特征
+def extract_features(pcap_file):
+    # 运行tranalyzer2命令行工具
+    command =  "t2 -r {pcap_file} -w {pcap_file}.csv -f features.csv"
+    subprocess.run(command, shell=True)
+
+    # 读取特征数据
+    features = pd.read_csv('features.csv', skiprows=6, header=None, delimiter=';', index_col=0)
+
+    # 删除无用列
+    features.drop([1, 2, 3, 4, 5], axis=1, inplace=True)
+
+    # 重命名列名
+    features.columns = ['duration', 'protocol', 'src_ip', 'src_port', 'dst_ip', 'dst_port', 'packets', 'bytes', 'flows',
+                        'flags', 'tos', 'class']
+
+    # 删除class列，因为我们不需要使用它
+    features.drop(['class'], axis=1, inplace=True)
+
+    return features
+
+
+# 获取所有pcap文件
+# pcap_files = []
+# for file in os.listdir('.'):
+#     if file.endswith('.pcap'):
+#         pcap_files.append(file)
+
+folder_path = "wcx-抓包-用于模型复现"
+pcap_files = []
+for file in glob.glob(os.path.join(folder_path, "*.pcap")):
+    pcap_files.append(file)
+
+# 提取所有pcap文件的特征
+features_list = []
+for pcap_file in pcap_files:
+    features = extract_features(pcap_file)
+    features_list.append(features)
+
+# 将所有特征合并成一个DataFrame
+all_features = pd.concat(features_list)
+
+# 标准化特征数据
+mean = all_features.mean()
+std = all_features.std()
+normalized_features = (all_features - mean) / std
+
+# 将标准化后的特征数据和SVM模型拟合
+X_train, X_test, y_train, y_test = train_test_split(normalized_features.values, np.zeros(len(normalized_features)),
+                                                    test_size=0.2, random_state=42)
+
+clf = SVC()
+
+# 使用交叉验证来评估模型性能
+scores = cross_val_score(clf, X_train, y_train, cv=5)
+print(f"Cross Validation Scores: {scores}")
+print(f"Mean Score: {np.mean(scores)}")
+print(f"Std Score: {np.std(scores)}")
+
+# 在测试集上测试并计算准确率
+clf.fit(X_train, y_train)
+y_pred = clf.predict(X_test)
+accuracy = accuracy_score(y_test, y_pred)
+print(f"Accuracy: {accuracy}")
+
+
+
+
--- a/test_3.py
+++ b/test_3.py
@@ -0,0 +1,10 @@
+# Name:fang xiaoyu
+# Time: 2023/3/11 11:31
+import os
+import glob
+
+folder_path = "wcx-抓包-用于模型复现"
+pcap_files = []
+for file in glob.glob(os.path.join(folder_path, "*.pcap")):
+    pcap_files.append(file)
+print(pcap_files)
--- a/test_5.py
+++ b/test_5.py
@@ -0,0 +1,233 @@
+# Name:fang xiaoyu
+# Time: 2023/3/11 20:10
+'''
+import cicflowmeter
+from scapy.all import *
+import requests
+#import pypcap
+import scipy
+
+cfm = cicflowmeter.CFM()
+# 读取pcap文件
+packets = rdpcap('/Users/fangxiaoyu/Desktop/VPN及其流量识别研究/抓包分析/wcx-抓包-用于模型复现/TorGuard_openvpnOverSSL.pcap')
+
+print(packets)
+for ts, pkt in packets:
+    cfm.flow_handler(pkt)
+
+result = cfm.get_result()
+'''
+
+'''
+from cicflowmeter.flow import Flow
+#from cicflowmeter.pcapy_reader import PcapyReader
+from scapy.all import *
+import csv
+
+# 定义pcap文件路径
+pcap_file = 'sample.pcap'
+
+# 创建PcapyReader对象
+pcap = rdpcap('20230309_fxy_psiphon_operation.pcapng')
+
+# 定义输出CSV文件路径
+output_file = 'output.csv'
+
+# 创建CSV文件对象并定义列名
+csv_file = open(output_file, 'w', newline='')
+csv_writer = csv.writer(csv_file)
+csv_writer.writerow(['src_ip', 'dst_ip', 'src_port', 'dst_port', 'proto', 'num_packets', 'bytes', 'duration', 'timestamp_start', 'timestamp_end', 'flags'])
+
+# 循环遍历每个数据包，并提取流特征，并将特征写入CSV文件
+for pkt in pcap:
+    flow = Flow(pkt, direction='B2A')
+    features = flow.features()
+    csv_writer.writerow([features['src_ip'], features['dst_ip'], features['src_port'], features['dst_port'], features['proto'], features['num_packets'], features['bytes'], features['duration'], features['timestamp_start'], features['timestamp_end'], features['flags']])
+
+# 关闭CSV文件
+csv_file.close()
+'''
+
+from datetime import datetime
+from pathlib import Path
+
+from cicflowmeter.flow import Flow
+#from cicflowmeter.reader import Reader
+from scapy.all import *
+import csv
+
+# 设置输入文件路径
+# 创建PcapyReader对象
+pcap = rdpcap('20230309_fxy_psiphon_operation.pcapng')
+
+# 设置输出文件路径
+output_file_path = "output.csv"
+
+# 创建CSV输出文件
+with open(output_file_path, mode='w', newline='') as output_file:
+    writer = csv.writer(output_file)
+
+    # 写入标题行
+    writer.writerow(
+        ['src_ip', 'dst_ip', 'src_port', 'dst_port', 'proto', 'num_packets', 'bytes', 'duration', 'timestamp_start',
+         'timestamp_end', 'flags'])
+
+    # 打开pcap文件并逐个处理数据包
+#with Reader(input_file_path) as reader:
+for pkt in pcap:
+            # 仅处理IP数据包
+    if pkt.haslayer('IP'):
+                # 创建Flow对象
+        flow = Flow(pkt,direction='B2A')
+
+                # 获取特征值列表
+        feature_values = flow.get_features()
+
+                # 将特征值列表写入CSV文件
+        writer.writerow(feature_values)
+
+'''
+from scapy.all import *
+
+# 读取pcap文件
+packets = rdpcap('/Users/fangxiaoyu/Desktop/VPN及其流量识别研究/抓包分析/wcx-抓包-用于模型复现/TorGuard_openvpnOverSSL.pcap')
+
+# 定义字典存储特征
+features = {}
+
+# 统计每个协议的数据包数量
+protocols = {}
+for pkt in packets:
+    if pkt.haslayer(IP):
+        protocol = pkt[IP].proto
+        if protocol not in protocols:
+            protocols[protocol] = 0
+        protocols[protocol] += 1
+for p in protocols:
+    features['protocol_{}'.format(p)] = protocols[p]
+
+# 统计每个源IP地址的数据包数量和大小
+src_ips = {}
+for pkt in packets:
+    if pkt.haslayer(IP):
+        src_ip = pkt[IP].src
+        if src_ip not in src_ips:
+            src_ips[src_ip] = {'count': 0, 'size': 0}
+        src_ips[src_ip]['count'] += 1
+        src_ips[src_ip]['size'] += len(pkt)
+for ip in src_ips:
+    features['src_ip_{}_count'.format(ip)] = src_ips[ip]['count']
+    features['src_ip_{}_size'.format(ip)] = src_ips[ip]['size']
+
+# 统计每个目的IP地址的数据包数量和大小
+dst_ips = {}
+for pkt in packets:
+    if pkt.haslayer(IP):
+        dst_ip = pkt[IP].dst
+        if dst_ip not in dst_ips:
+            dst_ips[dst_ip] = {'count': 0, 'size': 0}
+        dst_ips[dst_ip]['count'] += 1
+        dst_ips[dst_ip]['size'] += len(pkt)
+for ip in dst_ips:
+    features['dst_ip_{}_count'.format(ip)] = dst_ips[ip]['count']
+    features['dst_ip_{}_size'.format(ip)] = dst_ips[ip]['size']
+
+# 输出特征
+print(features)
+'''
+
+'''
+from scapy.all import *
+
+# 读取pcap文件
+pcap = rdpcap('/Users/fangxiaoyu/Desktop/VPN及其流量识别研究/抓包分析/wcx-抓包-用于模型复现/TorGuard_openvpnOverSSL.pcap')
+
+# 遍历数据包，提取流量特征
+for pkt in pcap:
+    # 数据包大小
+    pkt_size = len(pkt)
+
+    # IP地址
+    if IP in pkt:
+        src_ip = pkt[IP].src
+        dst_ip = pkt[IP].dst
+
+    # 协议类型
+    if TCP in pkt:
+        protocol = 'TCP'
+    elif UDP in pkt:
+        protocol = 'UDP'
+    elif ICMP in pkt:
+        protocol = 'ICMP'
+    else:
+        protocol = 'Other'
+
+    # 端口号
+    if TCP in pkt:
+        src_port = pkt[TCP].sport
+        dst_port = pkt[TCP].dport
+    elif UDP in pkt:
+        src_port = pkt[UDP].sport
+        dst_port = pkt[UDP].dport
+    else:
+        src_port = 0
+        dst_port = 0
+
+    # 输出流量特征
+    print(
+        'Packet Size: {}, Source IP: {}, Destination IP: {}, Protocol: {}, Source Port: {}, Destination Port: {}'.format(
+            pkt_size, src_ip, dst_ip, protocol, src_port, dst_port))
+'''
+
+'''
+from scapy.all import *
+import collections
+
+# 读取pcap文件
+packets = rdpcap('/Users/fangxiaoyu/Desktop/VPN及其流量识别研究/抓包分析/wcx-抓包-用于模型复现/TorGuard_openvpnOverSSL.pcap')
+
+# 计算数据包总数
+total_packets = len(packets)
+print("Total packets:", total_packets)
+
+# 计算不同协议类型的数据包数量
+protocols = collections.Counter([packet[IP].proto for packet in packets])
+print("Protocol counts:", protocols)
+
+# 查找源IP地址和目的IP地址
+for packet in packets:
+    if IP in packet:
+        src_ip = packet[IP].src
+        dst_ip = packet[IP].dst
+        print("Source IP:", src_ip)
+        print("Destination IP:", dst_ip)
+
+# 查找源MAC地址和目的MAC地址
+for packet in packets:
+    if Ether in packet:
+        src_mac = packet[Ether].src
+        dst_mac = packet[Ether].dst
+        print("Source MAC:", src_mac)
+        print("Destination MAC:", dst_mac)
+
+# 查找源端口号和目的端口号
+for packet in packets:
+    if TCP in packet:
+        src_port = packet[TCP].sport
+        dst_port = packet[TCP].dport
+        print("Source port:", src_port)
+        print("Destination port:", dst_port)
+
+# 计算数据包的平均大小
+total_size = sum(len(packet) for packet in packets)
+avg_size = total_size / total_packets
+print("Average packet size:", avg_size)
+
+# 查找HTTP请求
+for packet in packets:
+    if TCP in packet and packet[TCP].dport == 80 and packet.haslayer(Raw):
+        http_request = packet[Raw].load.decode()
+        print("HTTP request:", http_request)
+'''
+import  flowcontainer
+import cicflowmeter
--- a/test_cicflowmeter.py
+++ b/test_cicflowmeter.py
@@ -0,0 +1,6 @@
+# Name:fang xiaoyu
+# Time: 2023/3/15 16:00
+import cicflowmeter
+
+cicflowmeter
+
--- a/wcx-抓包-用于模型复现/.DS_Store
+++ b/wcx-抓包-用于模型复现/.DS_Store
--- a/wcx-抓包-用于模型复现/HotSpotshieldVPN_纯净流量.pcap
+++ b/wcx-抓包-用于模型复现/HotSpotshieldVPN_纯净流量.pcap
--- a/wcx-抓包-用于模型复现/TorGuard_openvpnOverSSL.pcap
+++ b/wcx-抓包-用于模型复现/TorGuard_openvpnOverSSL.pcap
--- a/wcx-抓包-用于模型复现/croptstorm_https_纯净流量.pcap
+++ b/wcx-抓包-用于模型复现/croptstorm_https_纯净流量.pcap
--- a/wcx-抓包-用于模型复现/croptstorm_ssh_纯净流量.pcap
+++ b/wcx-抓包-用于模型复现/croptstorm_ssh_纯净流量.pcap
--- a/wcx-抓包-用于模型复现/psiphonz纯净流量.pcap
+++ b/wcx-抓包-用于模型复现/psiphonz纯净流量.pcap
--- a/wcx-抓包-用于模型复现/sufshark_openvpn_tcp.pcap
+++ b/wcx-抓包-用于模型复现/sufshark_openvpn_tcp.pcap
--- a/wcx-抓包-用于模型复现/sufshark_t2.txt_flows.txt
+++ b/wcx-抓包-用于模型复现/sufshark_t2.txt_flows.txt
@@ -0,0 +1,77 @@
+%dir	flowInd	flowStat	timeFirst	timeLast	duration	numHdrDesc	numHdrs	hdrDesc	srcMac	dstMac	ethType	ethVlanID	srcIP	srcIPCC	srcIPOrg	srcPort	dstIP	dstIPCC	dstIPOrg	dstPort	l4Proto	macStat	macPairs	srcMac_dstMac_numP	srcMacLbl_dstMacLbl	dstPortClassN	dstPortClass	numPktsSnt	numPktsRcvd	numBytesSnt	numBytesRcvd	minPktSz	maxPktSz	avePktSize	stdPktSize	minIAT	maxIAT	aveIAT	stdIAT	pktps	bytps	pktAsm	bytAsm	tcpFStat	ipMindIPID	ipMaxdIPID	ipMinTTL	ipMaxTTL	ipTTLChg	ipToS	ipFlags	ipOptCnt	ipOptCpCl_Num	ip6OptCntHH_D	ip6OptHH_D	tcpISeqN	tcpPSeqCnt	tcpSeqSntBytes	tcpSeqFaultCnt	tcpPAckCnt	tcpFlwLssAckRcvdBytes	tcpAckFaultCnt	tcpBFlgtMx	tcpInitWinSz	tcpAveWinSz	tcpMinWinSz	tcpMaxWinSz	tcpWinSzDwnCnt	tcpWinSzUpCnt	tcpWinSzChgDirCnt	tcpWinSzThRt	tcpFlags	tcpAnomaly	tcpOptPktCnt	tcpOptCnt	tcpOptions	tcpMSS	tcpWS	tcpMPTBF	tcpMPF	tcpMPAID	tcpMPDSSF	tcpTmS	tcpTmER	tcpEcI	tcpUtm	tcpBtm	tcpSSASAATrip	tcpRTTAckTripMin	tcpRTTAckTripMax	tcpRTTAckTripAve	tcpRTTAckTripJitAve	tcpRTTSseqAA	tcpRTTAckJitAve	tcpStatesAFlags	icmpStat	icmpTCcnt	icmpBFTypH_TypL_Code	icmpTmGtw	icmpEchoSuccRatio	icmpPFindex	connSip	connDip	connSipDip	connSipDprt	connF
+A	5	0x0400000000004000	1678438540.671385	1678438541.327656	0.656271	1	3	eth:ipv4:tcp	0c:7a:15:dd:3e:7f	48:73:97:96:38:22	0x0800		192.168.58.75	07	"Private network"	60584	104.18.24.203	us	"Cloudflare"	443	6	0x00	1	0c:7a:15:dd:3e:7f_48:73:97:96:38:22_8	IntelCorp,MY_-	443	https	8	9	436	3154	0	181	54.5	64.1263	0	0.466433	0.08203387	0.1366479	12.19009	664.3597	-0.05882353	-0.7571031	0x0a11	1	1	128	128	0	0x00	0x0c40	0	0x00_0x00000000	0_0	0x00000000_0x00000000	2150689346	7	436	0	7	3155	0	181	64240	125269.3	64240	131072	2	1	3	0	0x011b	0x0000	1	6	0x0000001e	1460	256	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.063109	0.057313	0.466253	0.1553089	0.1551459	0.063237	0	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	9	1	2	46	5.111111
+B	5	0x0400000000004001	1678438540.734494	1678438541.385360	0.650866	1	3	eth:ipv4:tcp	48:73:97:96:38:22	0c:7a:15:dd:3e:7f	0x0800		104.18.24.203	us	"Cloudflare"	443	192.168.58.75	07	"Private network"	60584	6	0x00	1	48:73:97:96:38:22_0c:7a:15:dd:3e:7f_9	-_IntelCorp,MY	443	https	9	8	3154	436	0	1300	350.4445	438.1845	0	0.36255	0.07231844	0.1000702	13.82773	4845.852	0.05882353	0.7571031	0x0a11	1	56030	54	54	0	0x00	0x2040	0	0x00_0x00000000	0_0	0x00000000_0x00000000	1132434305	9	3154	0	9	436	0	2551	64240	65461.29	64240	65536	0	1	1	0	0x031b	0x0000	1	6	0x0000001e	1300	8192	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.000128	8.6e-05	0.001494	0.000680875	0.0005002746	0.1559898	0.1551467	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	1	9	1	45	45
+A	7	0x0400000000004000	1678438541.005057	1678438542.449668	1.444611	1	3	eth:ipv4:tcp	0c:7a:15:dd:3e:7f	48:73:97:96:38:22	0x0800		192.168.58.75	07	"Private network"	60585	104.18.121.34	us	"Cloudflare"	443	6	0x00	1	0c:7a:15:dd:3e:7f_48:73:97:96:38:22_9	IntelCorp,MY_-	443	https	9	10	1410	3976	0	1131	156.6667	323.5014	0	1.009822	0.1605123	0.2387844	6.230051	976.0413	-0.05263158	-0.4764203	0x0a11	1	1	128	128	0	0x00	0x0c40	0	0x00_0x00000000	0_0	0x00000000_0x00000000	3597570128	7	1410	0	7	3977	0	1131	64240	125008.2	64240	131072	2	1	3	0	0x011b	0x0001	2	12	0x0000001e	1460	256	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0	0.055063	0.192008	0.0849475	0.03365954	0.000218	0	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	8	1	28	44	5.5
+B	7	0x0400000000004001	1678438542.206887	1678438542.504731	0.297844	1	3	eth:ipv4:tcp	48:73:97:96:38:22	0c:7a:15:dd:3e:7f	0x0800		104.18.121.34	us	"Cloudflare"	443	192.168.58.75	07	"Private network"	60585	6	0x00	1	48:73:97:96:38:22_0c:7a:15:dd:3e:7f_10	-_IntelCorp,MY	443	https	10	9	3976	1410	0	1300	397.6	443.9756	0	0.099428	0.0297844	0.03162438	33.57462	13349.27	0.05263158	0.4764203	0x0a11	1	56724	54	54	0	0x00	0x2040	0	0x00_0x00000000	0_0	0x00000000_0x00000000	1713366050	10	3976	0	10	1410	0	2915	64240	65483.7	64240	65536	0	1	1	0	0x031b	0x0000	1	6	0x0000001e	1300	8192	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.000218	0.000218	0.007719	0.002232889	0.002696692	0.08718039	0.03376739	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	1	8	27	43	43
+A	9	0x0400000000004000	1678438542.715183	1678438542.947468	0.232285	1	3	eth:ipv4:tcp	0c:7a:15:dd:3e:7f	48:73:97:96:38:22	0x0800		192.168.58.75	07	"Private network"	60587	104.18.121.34	us	"Cloudflare"	443	6	0x00	1	0c:7a:15:dd:3e:7f_48:73:97:96:38:22_7	IntelCorp,MY_-	443	https	7	7	1795	495	0	1300	256.4286	387.302	0	0.110501	0.03318357	0.03569319	30.1354	7727.576	0	0.5676856	0x0a11	1	1	128	128	0	0x00	0x2c40	0	0x00_0x00000000	0_0	0x00000000_0x00000000	910259668	6	1795	0	6	496	0	1424	64240	122948.2	64240	131072	1	1	2	0	0x011b	0x0000	1	6	0x0000001e	1460	256	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.060424	0.055994	0.110257	0.07261272	0.02154457	0.060494	0	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	8	1	26	42	5.25
+B	9	0x0400000000004001	1678438542.775607	1678438543.003462	0.227855	1	3	eth:ipv4:tcp	48:73:97:96:38:22	0c:7a:15:dd:3e:7f	0x0800		104.18.121.34	us	"Cloudflare"	443	192.168.58.75	07	"Private network"	60587	6	0x00	1	48:73:97:96:38:22_0c:7a:15:dd:3e:7f_7	-_IntelCorp,MY	443	https	7	7	495	1795	0	380	70.71429	118.0025	0	0.058111	0.03255071	0.02319957	30.72129	2172.434	0	-0.5676856	0x0a11	1	34966	54	54	0	0x00	0x2040	0	0x00_0x00000000	0_0	0x00000000_0x00000000	2580614045	7	495	0	7	1795	0	380	64240	65383.53	64240	65536	0	1	1	0	0x031b	0x0000	1	6	0x0000001e	1300	8192	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	7e-05	7e-05	0.002117	0.0006821429	0.0006235491	0.07329486	0.02155359	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	1	8	25	41	41
+A	18	0x0400000000004000	1678438543.088988	1678438543.734788	0.645800	1	3	eth:ipv4:tcp	0c:7a:15:dd:3e:7f	48:73:97:96:38:22	0x0800		192.168.58.75	07	"Private network"	60591	18.156.144.91	us	"Amazon Technologies Inc"	80	6	0x00	1	0c:7a:15:dd:3e:7f_48:73:97:96:38:22_3	IntelCorp,MY_-	80	http	3	4	0	7	0	0	0	0	0	0.347355	0.2152667	0.1111793	4.645401	0	-0.1428571	-1	0x0a11	3	4	128	128	0	0x00	0x0c40	0	0x00_0x00000000	0_0	0x00000000_0x00000000	3963626404	2	0	0	2	8	0	0	64240	98324.32	64240	131072	0	1	1	0	0x0113	0x3000	1	6	0x0000001e	1460	256	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.346411	0.263105	0.346411	0.301331	0.02403587	0.347355	0	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	8	1	14	2	0.25
+B	18	0x0400000000004001	1678438543.435399	1678438543.997893	0.562494	1	3	eth:ipv4:tcp	48:73:97:96:38:22	0c:7a:15:dd:3e:7f	0x0800		18.156.144.91	us	"Amazon Technologies Inc"	80	192.168.58.75	07	"Private network"	60591	6	0x00	1	48:73:97:96:38:22_0c:7a:15:dd:3e:7f_4	-_IntelCorp,MY	80	http	4	3	7	0	0	7	1.75	2.277989	0	0.298848	0.1406235	0.108841	7.111187	12.44458	0.1428571	1	0x0a11	1	33828	42	42	0	0x00	0x2040	0	0x00_0x00000000	0_0	0x00000000_0x00000000	1452997980	4	7	0	4	0	0	7	26883	26965.12	26883	27008	0	1	1	0	0x031b	0x2000	1	6	0x0000001e	1300	128	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.000944	0.000541	0.000944	0.0008096667	0.0001551148	0.3021407	0.02403637	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	1	8	13	1	1
+A	19	0x0400000000004000	1678438543.090599	1678438543.734809	0.644210	1	3	eth:ipv4:tcp	0c:7a:15:dd:3e:7f	48:73:97:96:38:22	0x0800		192.168.58.75	07	"Private network"	60592	18.156.144.91	us	"Amazon Technologies Inc"	8443	6	0x00	1	0c:7a:15:dd:3e:7f_48:73:97:96:38:22_3	IntelCorp,MY_-	8443	pcsync-https	3	4	0	9	0	0	0	0	0	0.345841	0.2147367	0.1108992	4.656867	0	-0.1428571	-1	0x0a11	3	4	128	128	0	0x00	0x0c40	0	0x00_0x00000000	0_0	0x00000000_0x00000000	44257417	2	0	0	2	10	0	0	64240	98324.32	64240	131072	0	1	1	0	0x0113	0x3000	1	6	0x0000001e	1460	256	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.3448	0.263084	0.3448	0.3008745	0.023588	0.345841	0	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	8	1	12	2	0.25
+B	19	0x0400000000004001	1678438543.435399	1678438543.997893	0.562494	1	3	eth:ipv4:tcp	48:73:97:96:38:22	0c:7a:15:dd:3e:7f	0x0800		18.156.144.91	us	"Amazon Technologies Inc"	8443	192.168.58.75	07	"Private network"	60592	6	0x00	1	48:73:97:96:38:22_0c:7a:15:dd:3e:7f_4	-_IntelCorp,MY	8443	pcsync-https	4	3	9	0	0	9	2.25	2.928844	0	0.298848	0.1406235	0.108841	7.111187	16.00017	0.1428571	1	0x0a11	1	55859	42	42	0	0x00	0x2040	0	0x00_0x00000000	0_0	0x00000000_0x00000000	2775753545	4	9	0	4	0	0	9	26883	26965.12	26883	27008	0	1	1	0	0x031b	0x2000	1	6	0x0000001e	1300	128	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.001041	0.000562	0.001041	0.0008813333	0.0001843672	0.3017558	0.02358872	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	1	8	11	1	1
+A	20	0x0400000000004000	1678438543.090604	1678438543.734780	0.644176	1	3	eth:ipv4:tcp	0c:7a:15:dd:3e:7f	48:73:97:96:38:22	0x0800		192.168.58.75	07	"Private network"	60593	18.156.144.91	us	"Amazon Technologies Inc"	443	6	0x00	1	0c:7a:15:dd:3e:7f_48:73:97:96:38:22_3	IntelCorp,MY_-	443	https	3	4	0	8	0	0	0	0	0	0.345358	0.2147253	0.1108898	4.657112	0	-0.1428571	-1	0x0a11	1	3	128	128	0	0x00	0x0c40	0	0x00_0x00000000	0_0	0x00000000_0x00000000	4276855191	2	0	0	2	9	0	0	64240	98324.32	64240	131072	0	1	1	0	0x0113	0x3000	1	6	0x0000001e	1460	256	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.344795	0.251953	0.344795	0.2983295	0.02707363	0.345358	0	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	8	1	10	40	5
+B	20	0x0400000000004001	1678438543.435399	1678438543.986733	0.551334	1	3	eth:ipv4:tcp	48:73:97:96:38:22	0c:7a:15:dd:3e:7f	0x0800		18.156.144.91	us	"Amazon Technologies Inc"	443	192.168.58.75	07	"Private network"	60593	6	0x00	1	48:73:97:96:38:22_0c:7a:15:dd:3e:7f_4	-_IntelCorp,MY	443	https	4	3	8	0	0	8	2	2.603416	0	0.298848	0.1378335	0.1065318	7.25513	14.51026	0.1428571	1	0x0a11	1	60093	42	42	0	0x00	0x2040	0	0x00_0x00000000	0_0	0x00000000_0x00000000	1978569667	4	8	0	4	0	0	8	26883	26965.12	26883	27008	0	1	1	0	0x031b	0x2000	1	6	0x0000001e	1300	128	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.000563	0.000533	0.000563	0.000553	1.154699e-05	0.2988825	0.02707364	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	1	8	9	39	39
+A	22	0x0400000000004000	1678438544.090576	1678438544.446932	0.356356	1	3	eth:ipv4:tcp	0c:7a:15:dd:3e:7f	48:73:97:96:38:22	0x0800		192.168.58.75	07	"Private network"	60595	104.18.121.34	us	"Cloudflare"	443	6	0x00	1	0c:7a:15:dd:3e:7f_48:73:97:96:38:22_7	IntelCorp,MY_-	443	https	7	7	1795	495	0	1300	256.4286	387.302	0	0.158675	0.050908	0.05091392	19.64328	5037.098	0	0.5676856	0x0a11	1	1	128	128	0	0x00	0x2c40	0	0x00_0x00000000	0_0	0x00000000_0x00000000	1976837696	6	1795	0	6	496	0	1424	64240	122948.2	64240	131072	1	1	2	0	0x011b	0x0000	1	6	0x0000001e	1460	256	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.158433	0.057888	0.158433	0.09475029	0.03066983	0.158675	0	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	8	1	24	38	4.75
+B	22	0x0400000000004001	1678438544.249009	1678438544.504820	0.255811	1	3	eth:ipv4:tcp	48:73:97:96:38:22	0c:7a:15:dd:3e:7f	0x0800		104.18.121.34	us	"Cloudflare"	443	192.168.58.75	07	"Private network"	60595	6	0x00	1	48:73:97:96:38:22_0c:7a:15:dd:3e:7f_7	-_IntelCorp,MY	443	https	7	7	495	1795	0	380	70.71429	118.0025	0	0.067935	0.03654443	0.02641513	27.36395	1935.022	0	-0.5676856	0x0a11	1	9131	54	54	0	0x00	0x2040	0	0x00_0x00000000	0_0	0x00000000_0x00000000	4196101133	7	495	0	7	1795	0	380	64240	65383.53	64240	65536	0	1	1	0	0x031b	0x0000	1	6	0x0000001e	1300	8192	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.000242	0.000242	0.003362	0.001552	0.001161246	0.09630229	0.0306918	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	1	8	23	37	37
+A	24	0x0400000000004000	1678438544.498209	1678438544.926028	0.427819	1	3	eth:ipv4:tcp	0c:7a:15:dd:3e:7f	48:73:97:96:38:22	0x0800		192.168.58.75	07	"Private network"	60597	104.18.121.34	us	"Cloudflare"	443	6	0x00	1	0c:7a:15:dd:3e:7f_48:73:97:96:38:22_6	IntelCorp,MY_-	443	https	6	10	1558	1681	0	1187	259.6667	378.9682	0	0.306473	0.07130316	0.09652266	14.02462	3641.727	-0.25	-0.03797468	0x0a11	1	4	128	128	0	0x00	0x0c40	0	0x00_0x00000000	0_0	0x00000000_0x00000000	590772724	5	1558	0	5	1682	0	1187	64240	119839.5	64240	131072	0	1	1	0	0x011b	0x0000	1	6	0x0000001e	1460	256	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.056587	0.054003	0.305463	0.1849525	0.1112917	0.056837	0	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	8	1	22	36	4.5
+B	24	0x0400000000004001	1678438544.554796	1678438544.980031	0.425235	1	3	eth:ipv4:tcp	48:73:97:96:38:22	0c:7a:15:dd:3e:7f	0x0800		104.18.121.34	us	"Cloudflare"	443	192.168.58.75	07	"Private network"	60597	6	0x00	1	48:73:97:96:38:22_0c:7a:15:dd:3e:7f_10	-_IntelCorp,MY	443	https	10	6	1681	1558	0	1300	168.1	341.9332	0	0.205701	0.0425235	0.05742804	23.51641	3953.108	0.25	0.03797468	0x0a11	1	23974	54	54	0	0x00	0x2040	0	0x00_0x00000000	0_0	0x00000000_0x00000000	2786122804	10	1681	0	10	1558	0	1566	64240	65483.7	64240	65536	0	1	1	0	0x031b	0x0000	1	6	0x0000001e	1300	8192	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.00025	0.00025	0.003706	0.001621333	0.001240578	0.1865738	0.1112986	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	1	8	21	35	35
+A	23	0x0400000000004000	1678438544.498209	1678438544.926045	0.427836	1	3	eth:ipv4:tcp	0c:7a:15:dd:3e:7f	48:73:97:96:38:22	0x0800		192.168.58.75	07	"Private network"	60596	104.18.121.34	us	"Cloudflare"	443	6	0x00	1	0c:7a:15:dd:3e:7f_48:73:97:96:38:22_7	IntelCorp,MY_-	443	https	7	10	2122	2298	0	1300	303.1429	388.628	0	0.301446	0.06111943	0.09222254	16.36141	4959.844	-0.1764706	-0.03981901	0x0a11	1	4	128	128	0	0x00	0x2c40	0	0x00_0x00000000	0_0	0x00000000_0x00000000	706159185	6	2122	0	6	2299	0	1751	64240	123209.3	64240	131072	0	1	1	0	0x011b	0x0000	1	6	0x0000001e	1460	256	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.05928	0.05928	0.300588	0.1802012	0.1105553	0.059455	0	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	8	1	20	34	4.25
+B	23	0x0400000000004001	1678438544.557489	1678438544.986486	0.428997	1	3	eth:ipv4:tcp	48:73:97:96:38:22	0c:7a:15:dd:3e:7f	0x0800		104.18.121.34	us	"Cloudflare"	443	192.168.58.75	07	"Private network"	60596	6	0x00	1	48:73:97:96:38:22_0c:7a:15:dd:3e:7f_10	-_IntelCorp,MY	443	https	10	7	2298	2122	0	1300	229.8	378.1159	0	0.241122	0.0428997	0.06412552	23.31019	5356.681	0.1764706	0.03981901	0x0a11	1	13761	54	54	0	0x00	0x2040	0	0x00_0x00000000	0_0	0x00000000_0x00000000	3832647772	10	2298	0	10	2122	0	2183	64240	65483.7	64240	65536	0	1	1	0	0x031b	0x0000	1	6	0x0000001e	1300	8192	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.000175	0.000175	0.003424	0.001676571	0.001221098	0.1818778	0.110562	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	1	8	19	33	33
+A	26	0x0400000000004000	1678438545.126087	1678438545.629019	0.502932	1	3	eth:ipv4:tcp	0c:7a:15:dd:3e:7f	48:73:97:96:38:22	0x0800		192.168.58.75	07	"Private network"	60598	104.18.121.34	us	"Cloudflare"	443	6	0x00	1	0c:7a:15:dd:3e:7f_48:73:97:96:38:22_6	IntelCorp,MY_-	443	https	6	9	1586	1451	0	1215	264.3333	387.6433	0	0.284749	0.083822	0.08625923	11.93004	3153.508	-0.2	0.04445176	0x0a11	1	15	128	128	0	0x00	0x0c40	0	0x00_0x00000000	0_0	0x00000000_0x00000000	3173169355	5	1586	0	5	1452	0	1215	64240	119839.5	64240	131072	0	1	1	0	0x011b	0x0000	1	6	0x0000001e	1460	256	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.123596	0.055195	0.284336	0.1697037	0.09536102	0.123753	0	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	8	1	18	32	4
+B	26	0x0400000000004001	1678438545.249683	1678438545.684214	0.434531	1	3	eth:ipv4:tcp	48:73:97:96:38:22	0c:7a:15:dd:3e:7f	0x0800		104.18.121.34	us	"Cloudflare"	443	192.168.58.75	07	"Private network"	60598	6	0x00	1	48:73:97:96:38:22_0c:7a:15:dd:3e:7f_9	-_IntelCorp,MY	443	https	9	6	1451	1586	0	1300	161.2222	362.8199	0	0.185399	0.04828122	0.05557825	20.71199	3339.232	0.2	-0.04445176	0x0a11	1	56634	54	54	0	0x00	0x2040	0	0x00_0x00000000	0_0	0x00000000_0x00000000	14049914	9	1451	0	9	1586	0	1336	64240	65461.29	64240	65536	0	1	1	0	0x031b	0x0000	1	6	0x0000001e	1300	8192	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.000157	0.000157	0.034294	0.006473667	0.01139149	0.1761773	0.096039	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	1	8	17	31	31
+A	25	0x0400000000004000	1678438545.126087	1678438545.629019	0.502932	1	3	eth:ipv4:tcp	0c:7a:15:dd:3e:7f	48:73:97:96:38:22	0x0800		192.168.58.75	07	"Private network"	60599	104.18.121.34	us	"Cloudflare"	443	6	0x00	1	0c:7a:15:dd:3e:7f_48:73:97:96:38:22_7	IntelCorp,MY_-	443	https	7	7	1795	495	0	1300	256.4286	387.302	0	0.306882	0.07184743	0.09304016	13.91838	3569.071	0	0.5676856	0x0a11	1	18	128	128	0	0x00	0x2c40	0	0x00_0x00000000	0_0	0x00000000_0x00000000	3119053712	6	1795	0	6	496	0	1424	64240	122948.2	64240	131072	1	1	2	0	0x011b	0x0000	1	6	0x0000001e	1460	256	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.120159	0.052121	0.306644	0.136249	0.09884907	0.120329	0	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	8	1	16	30	3.75
+B	25	0x0400000000004001	1678438545.246246	1678438545.684214	0.437968	1	3	eth:ipv4:tcp	48:73:97:96:38:22	0c:7a:15:dd:3e:7f	0x0800		104.18.121.34	us	"Cloudflare"	443	192.168.58.75	07	"Private network"	60599	6	0x00	1	48:73:97:96:38:22_0c:7a:15:dd:3e:7f_7	-_IntelCorp,MY	443	https	7	7	495	1795	0	380	70.71429	118.0025	0	0.246967	0.06256685	0.07196545	15.9829	1130.22	0	-0.5676856	0x0a11	1	22152	54	54	0	0x00	0x2040	0	0x00_0x00000000	0_0	0x00000000_0x00000000	965130496	7	495	0	7	1795	0	380	64240	65383.53	64240	65536	0	1	1	0	0x031b	0x0000	1	6	0x0000001e	1300	8192	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.00017	0.00017	0.018521	0.003825143	0.005676229	0.1400741	0.0990119	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	1	8	15	29	29
+A	28	0x0400000000004000	1678438545.134879	1678438545.674793	0.539914	1	3	eth:ipv4:tcp	0c:7a:15:dd:3e:7f	48:73:97:96:38:22	0x0800		192.168.58.75	07	"Private network"	60601	104.18.121.34	us	"Cloudflare"	443	6	0x00	1	0c:7a:15:dd:3e:7f_48:73:97:96:38:22_6	IntelCorp,MY_-	443	https	6	8	1562	1389	0	1191	260.3333	380.2063	0	0.341996	0.08998567	0.1050285	11.11288	2893.053	-0.1428571	0.0586242	0x0a11	1	20	128	128	0	0x00	0x0c40	0	0x00_0x00000000	0_0	0x00000000_0x00000000	3133257095	5	1562	0	5	1390	0	1191	64240	119839.5	64240	131072	0	1	1	0	0x011b	0x0000	1	6	0x0000001e	1460	256	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.113621	0.05307	0.341878	0.176355	0.1177886	0.113792	0	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	8	1	14	28	3.5
+B	28	0x0400000000004001	1678438545.248500	1678438545.736766	0.488266	1	3	eth:ipv4:tcp	48:73:97:96:38:22	0c:7a:15:dd:3e:7f	0x0800		104.18.121.34	us	"Cloudflare"	443	192.168.58.75	07	"Private network"	60601	6	0x00	1	48:73:97:96:38:22_0c:7a:15:dd:3e:7f_8	-_IntelCorp,MY	443	https	8	6	1389	1562	0	1240	173.625	363.0067	0	0.239772	0.06103325	0.07001008	16.38451	2844.761	0.1428571	-0.0586242	0x0a11	1	64177	54	54	0	0x00	0x2040	0	0x00_0x00000000	0_0	0x00000000_0x00000000	3908520844	8	1389	0	8	1562	0	1274	64240	65429.27	64240	65536	0	1	1	0	0x031b	0x0000	1	6	0x0000001e	1300	8192	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.000171	0.000118	0.025695	0.005073167	0.008476242	0.1814282	0.1180932	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	1	8	13	27	27
+A	27	0x0400000000004000	1678438545.126130	1678438545.685652	0.559522	1	3	eth:ipv4:tcp	0c:7a:15:dd:3e:7f	48:73:97:96:38:22	0x0800		192.168.58.75	07	"Private network"	60600	104.18.121.34	us	"Cloudflare"	443	6	0x00	1	0c:7a:15:dd:3e:7f_48:73:97:96:38:22_6	IntelCorp,MY_-	443	https	6	15	1571	7129	0	1200	261.8333	382.9936	0	0.360885	0.09325366	0.1132214	10.72344	2807.754	-0.4285714	-0.6388506	0x0a11	1	20	128	128	0	0x00	0x0c40	0	0x00_0x00000000	0_0	0x00000000_0x00000000	234773166	5	1571	0	5	7130	0	1200	64240	119839.5	64240	131072	0	1	1	0	0x011b	0x0000	1	6	0x0000001e	1460	256	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.129368	0.063509	0.360639	0.2688299	0.1214907	0.129584	0	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	8	1	12	26	3.25
+B	27	0x0400000000004001	1678438545.255498	1678438545.752258	0.496760	1	3	eth:ipv4:tcp	48:73:97:96:38:22	0c:7a:15:dd:3e:7f	0x0800		104.18.121.34	us	"Cloudflare"	443	192.168.58.75	07	"Private network"	60600	6	0x00	1	48:73:97:96:38:22_0c:7a:15:dd:3e:7f_15	-_IntelCorp,MY	443	https	15	6	7129	1571	0	1300	475.2667	547.9085	0	0.257573	0.03311734	0.06163195	30.19567	14350.99	0.4285714	0.6388506	0x0a11	1	33106	54	54	0	0x00	0x2040	0	0x00_0x00000000	0_0	0x00000000_0x00000000	642794098	15	7129	0	15	1571	0	7014	64240	65527.21	64240	65536	0	1	1	0	0x031b	0x0000	1	6	0x0000001e	1300	8192	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.000216	0.000216	0.003528	0.001114	0.001019898	0.2699439	0.121495	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	1	8	11	25	25
+A	34	0x0400000000004000	1678438545.675960	1678438545.885973	0.210013	1	3	eth:ipv4:tcp	0c:7a:15:dd:3e:7f	48:73:97:96:38:22	0x0800		192.168.58.75	07	"Private network"	60606	104.18.121.34	us	"Cloudflare"	443	6	0x00	1	0c:7a:15:dd:3e:7f_48:73:97:96:38:22_8	IntelCorp,MY_-	443	https	8	12	1825	4102	0	1300	228.125	370.2347	0	0.083209	0.02625163	0.02938896	38.09288	8689.938	-0.2	-0.3841741	0x0a11	1	4	128	128	0	0x00	0x2c40	0	0x00_0x00000000	0_0	0x00000000_0x00000000	375133525	7	1825	0	7	4103	0	1454	64240	125568.1	64240	131072	0	1	1	0	0x011b	0x0000	1	6	0x0000001e	1460	256	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.060806	0.000185	0.082718	0.05933783	0.02698222	0.061112	0	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	8	1	10	24	3
+B	34	0x0400000000004001	1678438545.736766	1678438545.942381	0.205615	1	3	eth:ipv4:tcp	48:73:97:96:38:22	0c:7a:15:dd:3e:7f	0x0800		104.18.121.34	us	"Cloudflare"	443	192.168.58.75	07	"Private network"	60606	6	0x00	1	48:73:97:96:38:22_0c:7a:15:dd:3e:7f_12	-_IntelCorp,MY	443	https	12	8	4102	1825	0	1300	341.8333	486.1904	0	0.062715	0.01713458	0.02247545	58.3615	19949.91	0.2	0.3841741	0x0a11	1	1768	54	54	0	0x00	0x2040	0	0x00_0x00000000	0_0	0x00000000_0x00000000	1373350812	12	4102	0	12	1825	0	3953	64240	65510.38	64240	65536	0	1	1	0	0x031b	0x0000	1	6	0x0000001e	1300	8192	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.000306	0.000306	0.003415	0.0012425	0.001121137	0.06058033	0.0270055	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	1	8	9	23	23
+A	29	0x0400000000004000	1678438545.135080	1678438546.063334	0.928254	1	3	eth:ipv4:tcp	0c:7a:15:dd:3e:7f	48:73:97:96:38:22	0x0800		192.168.58.75	07	"Private network"	60602	104.18.121.34	us	"Cloudflare"	443	6	0x00	1	0c:7a:15:dd:3e:7f_48:73:97:96:38:22_6	IntelCorp,MY_-	443	https	6	8	1557	1209	0	1186	259.5	378.6587	0	0.721036	0.154709	0.2286772	6.463748	1677.343	-0.1428571	0.1258135	0x0a11	1	40	128	128	0	0x00	0x0c40	0	0x00_0x00000000	0_0	0x00000000_0x00000000	1391787073	5	1557	0	5	1210	0	1186	64240	119317.3	64240	131072	1	1	2	0	0x011b	0x0000	1	6	0x0000001e	1460	256	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.116267	0.056845	0.720934	0.3301695	0.2761069	0.116462	0	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	8	1	8	22	2.75
+B	29	0x0400000000004001	1678438545.251347	1678438546.214962	0.963615	1	3	eth:ipv4:tcp	48:73:97:96:38:22	0c:7a:15:dd:3e:7f	0x0800		104.18.121.34	us	"Cloudflare"	443	192.168.58.75	07	"Private network"	60602	6	0x00	1	48:73:97:96:38:22_0c:7a:15:dd:3e:7f_8	-_IntelCorp,MY	443	https	8	6	1209	1557	0	1060	151.125	309.8497	0	0.623965	0.1204519	0.1791396	8.302071	1254.651	0.1428571	-0.1258135	0x0a11	1	38713	54	54	0	0x00	0x2040	0	0x00_0x00000000	0_0	0x00000000_0x00000000	907775662	8	1209	0	8	1557	0	1094	64240	65429.27	64240	65536	0	1	1	0	0x031b	0x0000	1	6	0x0000001e	1300	8192	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.000195	0.000102	0.030043	0.005783333	0.009948266	0.3359528	0.2762861	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	1	8	7	21	21
+A	36	0x0400000000004000	1678438562.121152	1678438562.409247	0.288095	1	3	eth:ipv4:tcp	0c:7a:15:dd:3e:7f	48:73:97:96:38:22	0x0800		192.168.58.75	07	"Private network"	60608	104.18.121.34	us	"Cloudflare"	443	6	0x00	1	0c:7a:15:dd:3e:7f_48:73:97:96:38:22_7	IntelCorp,MY_-	443	https	7	7	1795	495	0	1300	256.4286	387.302	0	0.12233	0.04115643	0.0417183	24.29754	6230.583	0	0.5676856	0x0a11	1	1	128	128	0	0x00	0x2c40	0	0x00_0x00000000	0_0	0x00000000_0x00000000	1427192076	6	1795	0	6	496	0	1424	64240	122948.2	64240	131072	1	1	2	0	0x011b	0x0000	1	6	0x0000001e	1460	256	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.100033	0.056673	0.122011	0.08308142	0.02462088	0.10012	0	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	9	1	8	22	2.444444
+B	36	0x0400000000004001	1678438562.221185	1678438562.471298	0.250113	1	3	eth:ipv4:tcp	48:73:97:96:38:22	0c:7a:15:dd:3e:7f	0x0800		104.18.121.34	us	"Cloudflare"	443	192.168.58.75	07	"Private network"	60608	6	0x00	1	48:73:97:96:38:22_0c:7a:15:dd:3e:7f_7	-_IntelCorp,MY	443	https	7	7	495	1795	0	380	70.71429	118.0025	0	0.065366	0.03573043	0.02573201	27.98735	1979.105	0	-0.5676856	0x0a11	1	52615	54	54	0	0x00	0x2040	0	0x00_0x00000000	0_0	0x00000000_0x00000000	3018865306	7	495	0	7	1795	0	380	64240	65383.53	64240	65536	0	1	1	0	0x031b	0x0000	1	6	0x0000001e	1300	8192	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	8.7e-05	8.7e-05	0.003315	0.001106429	0.001020167	0.08418785	0.024642	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	1	9	7	21	21
+A	12	0x0400000000004000	1678438542.912026	1678438648.262335	105.350309	1	3	eth:ipv4:tcp	0c:7a:15:dd:3e:7f	48:73:97:96:38:22	0x0800		192.168.58.75	07	"Private network"	60589	52.232.209.85	us	"Microsoft Corporation"	443	6	0x00	1	0c:7a:15:dd:3e:7f_48:73:97:96:38:22_11	IntelCorp,MY_-	443	https	11	14	1208	5717	0	598	109.8182	168.0854	0	75.01094	9.577302	20.72395	0.1044135	11.46651	-0.12	-0.6511191	0x0a11	1	5	128	128	0	0x00	0x0c40	0	0x00_0x00000000	0_0	0x00000000_0x00000000	1921833801	10	1208	0	10	5718	0	598	64240	89622.09	0	131072	3	1	4	0	0x041e	0x0000	1	6	0x0000001e	1460	256	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.266281	0.264602	75.01041	11.45516	24.96668	0.26678	0	0x42	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	9	1	4	20	2.222222
+B	12	0x0400000000004001	1678438543.178307	1678438623.300547	80.122240	1	3	eth:ipv4:tcp	48:73:97:96:38:22	0c:7a:15:dd:3e:7f	0x0800		52.232.209.85	us	"Microsoft Corporation"	443	192.168.58.75	07	"Private network"	60589	6	0x00	1	48:73:97:96:38:22_0c:7a:15:dd:3e:7f_14	-_IntelCorp,MY	443	https	14	11	5717	1208	0	1300	408.3571	493.3763	0	75.05939	5.723017	18.4808	0.174733	71.35347	0.12	0.6511191	0x0a11	1	52359	39	39	0	0x00	0x2040	0	0x00_0x00000000	0_0	0x00000000_0x00000000	158438382	14	5717	0	14	1208	0	5057	64240	64509.36	64240	64512	0	1	1	0	0x031b	0x0000	1	6	0x0000001e	1300	1024	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.000499	0.000376	24.96179	2.287668	6.836562	13.74283	25.88578	0x02	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	1	9	3	19	19
+A	13	0x0400000000004000	1678438542.912026	1678438648.263297	105.351271	1	3	eth:ipv4:tcp	0c:7a:15:dd:3e:7f	48:73:97:96:38:22	0x0800		192.168.58.75	07	"Private network"	60590	52.232.209.85	us	"Microsoft Corporation"	443	6	0x00	1	0c:7a:15:dd:3e:7f_48:73:97:96:38:22_10	IntelCorp,MY_-	443	https	10	14	1280	5708	0	670	128	190.221	0	75.01015	10.53513	21.76739	0.09492055	12.14983	-0.1666667	-0.6336577	0x0a11	1	5	128	128	0	0x00	0x0c40	0	0x00_0x00000000	0_0	0x00000000_0x00000000	649951714	9	1280	0	9	5709	0	670	64240	88825.92	0	131072	3	1	4	0	0x041e	0x0000	1	6	0x0000001e	1460	256	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.266281	0.255916	75.00959	10.97921	25.14949	0.266642	0	0x42	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	9	1	2	18	2
+B	13	0x0400000000004001	1678438543.178307	1678438619.828499	76.650192	1	3	eth:ipv4:tcp	48:73:97:96:38:22	0c:7a:15:dd:3e:7f	0x0800		52.232.209.85	us	"Microsoft Corporation"	443	192.168.58.75	07	"Private network"	60590	6	0x00	1	48:73:97:96:38:22_0c:7a:15:dd:3e:7f_14	-_IntelCorp,MY	443	https	14	10	5708	1280	0	1300	407.7143	493.5257	0	75.06441	5.475013	18.54427	0.1826479	74.46818	0.1666667	0.6336577	0x0a11	1	52858	40	40	0	0x00	0x2040	0	0x00_0x00000000	0_0	0x00000000_0x00000000	979930531	14	5708	0	14	1280	0	5057	64240	64509.36	64240	64512	0	1	1	0	0x031b	0x0000	1	6	0x0000001e	1300	1024	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.000361	0.000361	28.4348	2.850353	8.090526	13.82956	26.41881	0x02	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	1	9	1	17	17
+A	11	0x0400000000004000	1678438542.810151	1678438643.277813	100.467662	1	3	eth:ipv4:tcp	0c:7a:15:dd:3e:7f	48:73:97:96:38:22	0x0800		192.168.58.75	07	"Private network"	60588	1.1.1.1	us	"APNIC Research and Development"	443	6	0x00	1	0c:7a:15:dd:3e:7f_48:73:97:96:38:22_9	IntelCorp,MY_-	443	https	9	8	401	3634	0	155	44.55556	57.09469	0	99.96101	11.16307	29.37076	0.08958106	3.991334	0.05882353	-0.8012391	0x0a11	1	1	128	128	0	0x00	0x0c40	0	0x00_0x00000000	0_0	0x00000000_0x00000000	3908542269	8	401	0	8	3634	0	155	64240	126688.4	64240	131072	2	1	3	0	0x011b	0x0000	1	6	0x0000001e	1460	256	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.055786	0.053239	0.222275	0.0758505	0.05177466	0.055936	0	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	8	1	2	16	2
+B	11	0x0400000000004001	1678438542.865937	1678438643.277636	100.411699	1	3	eth:ipv4:tcp	48:73:97:96:38:22	0c:7a:15:dd:3e:7f	0x0800		1.1.1.1	us	"APNIC Research and Development"	443	192.168.58.75	07	"Private network"	60588	6	0x00	1	48:73:97:96:38:22_0c:7a:15:dd:3e:7f_8	-_IntelCorp,MY	443	https	8	9	3634	401	0	1300	454.25	444.2963	0	100.238	12.55146	31.00189	0.07967199	36.191	-0.05882353	0.8012391	0x0a11	1	24054	54	54	0	0x00	0x2040	0	0x00_0x00000000	0_0	0x00000000_0x00000000	2250847198	8	3634	0	8	401	0	2795	64240	65429.27	64240	65536	0	1	1	0	0x031b	0x0000	1	6	0x0000001e	1300	8192	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.00015	0.00015	100.0157	11.12014	29.40311	11.19599	29.40316	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	1	8	1	15	15
+A	21	0x0400000000004000	1678438543.770104	1678438644.309084	100.538980	1	3	eth:ipv4:tcp	0c:7a:15:dd:3e:7f	48:73:97:96:38:22	0x0800		192.168.58.75	07	"Private network"	60594	104.18.120.34	us	"Cloudflare"	443	6	0x00	1	0c:7a:15:dd:3e:7f_48:73:97:96:38:22_7	IntelCorp,MY_-	443	https	7	6	555	908	0	371	79.28571	113.6315	0	99.95834	14.36271	31.91214	0.06962474	5.520247	0.07692308	-0.241285	0x0a11	1	18	128	128	0	0x00	0x0c40	0	0x00_0x00000000	0_0	0x00000000_0x00000000	1070609892	6	555	0	6	908	0	371	64240	122704.7	64240	131072	1	1	2	0	0x011b	0x0000	1	6	0x0000001e	1460	256	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.065768	0.062417	0.22141	0.1155597	0.05472611	0.06597301	0	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	7	1	6	14	2
+B	21	0x0400000000004001	1678438543.835872	1678438644.308841	100.472969	1	3	eth:ipv4:tcp	48:73:97:96:38:22	0c:7a:15:dd:3e:7f	0x0800		104.18.120.34	us	"Cloudflare"	443	192.168.58.75	07	"Private network"	60594	6	0x00	1	48:73:97:96:38:22_0c:7a:15:dd:3e:7f_6	-_IntelCorp,MY	443	https	6	7	908	555	0	793	151.3333	259.2991	0	100.234	16.74549	34.08405	0.05971755	9.037256	-0.07692308	0.241285	0x0a11	1	52845	54	54	0	0x00	0x0040	0	0x00_0x00000000	0_0	0x00000000_0x00000000	1942470182	6	908	0	6	555	0	793	64240	65318.18	64240	65536	0	1	1	0	0x031b	0x0000	1	6	0x0000001e	1300	8192	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.000205	0.000205	100.0126	14.29592	31.95741	14.41148	31.95745	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	1	7	5	13	13
+A	32	0x0400000000004000	1678438545.258032	1678438645.662774	100.404742	1	3	eth:ipv4:tcp	0c:7a:15:dd:3e:7f	48:73:97:96:38:22	0x0800		192.168.58.75	07	"Private network"	60604	104.18.120.34	us	"Cloudflare"	443	6	0x00	1	0c:7a:15:dd:3e:7f_48:73:97:96:38:22_10	IntelCorp,MY_-	443	https	10	15	430	10948	0	185	43	59.99718	0	100.0151	10.04047	28.27848	0.0995969	4.282666	-0.2	-0.9244155	0x0a11	1	9	128	128	0	0x00	0x0c40	0	0x00_0x00000000	0_0	0x00000000_0x00000000	3630287932	9	430	0	9	10948	0	185	64240	128356.6	64240	131072	1	2	2	0	0x011b	0x0000	1	6	0x0000001e	1460	256	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.056804	0.000396	0.117192	0.0596792	0.04418295	0.057023	0	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	7	1	4	12	1.714286
+B	32	0x0400000000004001	1678438545.314836	1678438645.662560	100.347724	1	3	eth:ipv4:tcp	48:73:97:96:38:22	0c:7a:15:dd:3e:7f	0x0800		104.18.120.34	us	"Cloudflare"	443	192.168.58.75	07	"Private network"	60604	6	0x00	1	48:73:97:96:38:22_0c:7a:15:dd:3e:7f_15	-_IntelCorp,MY	443	https	15	10	10948	430	0	1300	729.8666	552.1032	0	100.074	6.689848	24.11171	0.1494802	109.1006	0.2	0.9244155	0x0a11	1	31044	54	54	0	0x00	0x2040	0	0x00_0x00000000	0_0	0x00000000_0x00000000	1309126645	15	10948	0	15	430	0	5357	64240	65527.21	64240	65536	0	1	1	0	0x031b	0x0000	1	6	0x0000001e	1300	8192	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.000219	0.000214	100.0155	10.00332	28.29028	10.063	28.29031	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	1	7	3	11	11
+A	3	0x0400000000004000	1678438540.431238	1678438647.705338	107.274100	1	3	eth:ipv4:tcp	0c:7a:15:dd:3e:7f	48:73:97:96:38:22	0x0800		192.168.58.75	07	"Private network"	60582	104.18.12.101	us	"Cloudflare"	443	6	0x00	1	0c:7a:15:dd:3e:7f_48:73:97:96:38:22_19	IntelCorp,MY_-	443	https	19	18	1528	10675	0	201	80.42105	80.89372	0	100.0215	5.646005	21.64643	0.1771164	14.24389	0.02702703	-0.7495698	0x0a11	1	1	128	128	0	0x00	0x0c40	0	0x00_0x00000000	0_0	0x00000000_0x00000000	3683916154	18	1528	0	18	10675	0	201	64240	130512.8	64240	131072	5	4	6	0	0x011b	0x0000	1	6	0x0000001e	1460	256	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.059004	0.057066	0.070825	0.06399962	0.003265371	0.059128	0	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	7	1	2	10	1.428571
+B	3	0x0400000000004001	1678438540.490242	1678438647.705098	107.214856	1	3	eth:ipv4:tcp	48:73:97:96:38:22	0c:7a:15:dd:3e:7f	0x0800		104.18.12.101	us	"Cloudflare"	443	192.168.58.75	07	"Private network"	60582	6	0x00	1	48:73:97:96:38:22_0c:7a:15:dd:3e:7f_18	-_IntelCorp,MY	443	https	18	19	10675	1528	0	1300	593.0555	461.2215	0	100.0903	5.956381	22.21859	0.1678872	99.56642	-0.02702703	0.7495698	0x0a11	1	39561	54	54	0	0x00	0x2040	0	0x00_0x00000000	0_0	0x00000000_0x00000000	1916516677	18	10675	0	18	1528	0	4406	64240	65532.99	64240	65536	0	1	1	0	0x031b	0x0000	1	6	0x0000001e	1300	8192	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.000124	9.2e-05	100.0219	5.61395	21.65435	5.677949	21.65435	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	1	7	1	9	9
+A	31	0x0400000000004000	1678438545.171929	1678438545.589839	0.417910	1	3	eth:ipv4:tcp	0c:7a:15:dd:3e:7f	48:73:97:96:38:22	0x0800		192.168.58.75	07	"Private network"	60603	104.18.121.34	us	"Cloudflare"	443	6	0x00	1	0c:7a:15:dd:3e:7f_48:73:97:96:38:22_15	IntelCorp,MY_-	443	https	15	69	1580	81005	0	1209	105.3333	272.6754	0	0.120224	0.02786067	0.03565436	35.89289	3780.718	-0.6428571	-0.9617364	0x0a11	1	9	128	128	0	0x00	0x0c40	0	0x00_0x00000000	0_0	0x00000000_0x00000000	3715891804	14	1580	0	14	81006	0	1209	64240	130618.7	64240	131072	0	1	1	0	0x011b	0x0000	1	6	0x0000001e	1460	256	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.080662	0.000195	0.118318	0.0612336	0.04713053	0.080904	0	0x02	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	6	1	6	8	1.333333
+B	31	0x0400000000004001	1678438545.252591	1678438545.647904	0.395313	1	3	eth:ipv4:tcp	48:73:97:96:38:22	0c:7a:15:dd:3e:7f	0x0800		104.18.121.34	us	"Cloudflare"	443	192.168.58.75	07	"Private network"	60603	6	0x00	1	48:73:97:96:38:22_0c:7a:15:dd:3e:7f_69	-_IntelCorp,MY	443	https	69	15	81005	1580	0	1300	1173.985	357.1899	0	0.118578	0.005729174	0.01953879	174.5452	204913.6	0.6428571	0.9617364	0x0a11	1	30463	54	54	0	0x00	0x2040	0	0x00_0x00000000	0_0	0x00000000_0x00000000	3736356320	69	81006	0	69	1580	0	24700	64240	65536	64240	65536	0	1	1	0	0x031b	0x0000	1	6	0x0000001e	1300	8192	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.000242	0.000242	0.003786	0.001056533	0.001083837	0.06229014	0.04714299	0x02	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	1	6	5	7	7
+A	33	0x0400000000004000	1678438545.675645	1678438545.891976	0.216331	1	3	eth:ipv4:tcp	0c:7a:15:dd:3e:7f	48:73:97:96:38:22	0x0800		192.168.58.75	07	"Private network"	60605	104.18.121.34	us	"Cloudflare"	443	6	0x00	1	0c:7a:15:dd:3e:7f_48:73:97:96:38:22_10	IntelCorp,MY_-	443	https	10	24	1821	20317	0	1300	182.1	342.2162	0	0.081293	0.0216331	0.0272793	46.22546	8417.656	-0.4117647	-0.8354865	0x0a11	1	4	128	128	0	0x00	0x2c40	0	0x00_0x00000000	0_0	0x00000000_0x00000000	2365389818	9	1821	0	9	20318	0	1450	64240	128375.1	64240	131072	0	1	1	0	0x011b	0x0000	1	6	0x0000001e	1460	256	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.061121	0.000293	0.080951	0.04020167	0.03495514	0.061352	0	0x02	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	6	1	4	6	1
+B	33	0x0400000000004001	1678438545.736766	1678438545.946624	0.209858	1	3	eth:ipv4:tcp	48:73:97:96:38:22	0c:7a:15:dd:3e:7f	0x0800		104.18.121.34	us	"Cloudflare"	443	192.168.58.75	07	"Private network"	60605	6	0x00	1	48:73:97:96:38:22_0c:7a:15:dd:3e:7f_24	-_IntelCorp,MY	443	https	24	10	20317	1821	0	1300	846.5417	562.1317	0	0.062715	0.008744082	0.0177017	114.363	96813.09	0.4117647	0.8354865	0x0a11	1	8119	54	54	0	0x00	0x2040	0	0x00_0x00000000	0_0	0x00000000_0x00000000	768290641	24	20318	0	24	1821	0	7996	64240	65535.64	64240	65536	0	1	1	0	0x031b	0x0000	1	6	0x0000001e	1300	8192	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.000231	0.000231	0.003412	0.0010586	0.001067795	0.04126027	0.03497144	0x02	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	1	6	3	5	5
+A	2	0x0400000000004000	1678438540.371353	1678438664.811777	124.440424	1	3	eth:ipv4:tcp	0c:7a:15:dd:3e:7f	48:73:97:96:38:22	0x0800		192.168.58.75	07	"Private network"	60581	104.18.120.34	us	"Cloudflare"	443	6	0x00	1	0c:7a:15:dd:3e:7f_48:73:97:96:38:22_19	IntelCorp,MY_-	443	https	19	22	5760	4335	0	1215	303.1579	383.8278	0	99.96812	6.549496	21.72406	0.1526835	46.28721	-0.07317073	0.141159	0x0a11	1	6	128	128	0	0x00	0x0c40	0	0x00_0x00000000	0_0	0x00000000_0x00000000	508492307	18	5760	0	18	4335	0	1215	64240	130521.4	64240	131072	6	2	7	0	0x011b	0x0000	1	6	0x0000001e	1460	256	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.053018	0.052095	0.980388	0.1485053	0.1976676	0.053173	0	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	6	1	2	4	0.6666667
+B	2	0x0400000000004001	1678438540.424371	1678438664.811539	124.387168	1	3	eth:ipv4:tcp	48:73:97:96:38:22	0c:7a:15:dd:3e:7f	0x0800		104.18.120.34	us	"Cloudflare"	443	192.168.58.75	07	"Private network"	60581	6	0x00	1	48:73:97:96:38:22_0c:7a:15:dd:3e:7f_22	-_IntelCorp,MY	443	https	22	19	4335	5760	0	1300	197.0455	326.2524	0	100.0845	5.653963	20.44267	0.1768671	34.85086	0.07317073	-0.141159	0x0a11	1	865	54	54	0	0x00	0x2040	0	0x00_0x00000000	0_0	0x00000000_0x00000000	718954642	22	4335	0	22	5760	0	2537	64240	65535.27	64240	65536	0	1	1	0	0x031b	0x0000	1	6	0x0000001e	1300	8192	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.000155	0.000155	100.0222	6.434432	21.76769	6.582937	21.76858	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	1	6	1	3	3
+A	35	0x0400000000004000	1678438546.055998	1678438675.920053	129.864055	1	3	eth:ipv4:tcp	0c:7a:15:dd:3e:7f	48:73:97:96:38:22	0x0800		192.168.58.75	07	"Private network"	60607	104.18.121.34	us	"Cloudflare"	443	6	0x00	1	0c:7a:15:dd:3e:7f_48:73:97:96:38:22_26	IntelCorp,MY_-	443	https	26	32	12057	2048	0	1235	463.7308	463.8277	0	99.965	4.994771	18.84311	0.2002094	92.84324	-0.1034483	0.7096065	0x0a11	1	8	128	128	0	0x00	0x0c40	0	0x00_0x00000000	0_0	0x00000000_0x00000000	1152582751	24	12057	0	24	2048	0	1235	64240	130622.2	64240	131072	7	2	8	0	0x011b	0x0001	2	12	0x0000001e	1460	256	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0	0.053696	0.894047	0.1737839	0.1948059	7.9e-05	0	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	5	1	2	2	0.4
+B	35	0x0400000000004001	1678438547.215075	1678438675.919813	128.704738	1	3	eth:ipv4:tcp	48:73:97:96:38:22	0c:7a:15:dd:3e:7f	0x0800		104.18.121.34	us	"Cloudflare"	443	192.168.58.75	07	"Private network"	60607	6	0x00	1	48:73:97:96:38:22_0c:7a:15:dd:3e:7f_32	-_IntelCorp,MY	443	https	32	26	2048	12057	0	218	64	81.9301	0	100.0767	4.022023	17.19595	0.2486311	15.91239	0.1034483	-0.7096065	0x0a11	1	44156	54	54	0	0x00	0x2040	0	0x00_0x00000000	0_0	0x00000000_0x00000000	208830810	32	2048	0	32	12057	0	247	64240	65535.98	64240	65536	0	1	1	0	0x031b	0x0000	1	6	0x0000001e	1300	8192	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	7.9e-05	7.9e-05	100.0166	4.810813	18.89779	4.984597	18.8988	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	1	5	1	1	1
+A	1	0x0400000000004000	1678438540.148032	1678438540.148032	0.000000	1	3	eth:ipv4:udp	0c:7a:15:dd:3e:7f	48:73:97:96:38:22	0x0800		192.168.58.75	07	"Private network"	51976	192.168.44.40	07	"Private network"	53	17	0x00	1	0c:7a:15:dd:3e:7f_48:73:97:96:38:22_1	IntelCorp,MY_-	53	domain	1	1	34	66	34	34	34	0	0	0	0	0	0	0	0	-0.32	0x0001	65535	0	128	128	0	0x00	0x0c00	0	0x00_0x00000000	0_0	0x00000000_0x00000000	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0x0000	0x0000	0	0	0x00000000	0	1	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.205926	0.205926	0.205926	0.205926	0	0.205926	0	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	4	1	12	12	3
+B	1	0x0400000000004001	1678438540.353958	1678438540.353958	0.000000	1	3	eth:ipv4:udp	48:73:97:96:38:22	0c:7a:15:dd:3e:7f	0x0800		192.168.44.40	07	"Private network"	53	192.168.58.75	07	"Private network"	51976	17	0x00	1	48:73:97:96:38:22_0c:7a:15:dd:3e:7f_1	-_IntelCorp,MY	53	domain	1	1	66	34	66	66	66	0	0	0	0	0	0	0	0	0.32	0x0001	65535	0	63	63	0	0x00	0x0040	0	0x00_0x00000000	0_0	0x00000000_0x00000000	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0x0000	0x0000	0	0	0x00000000	0	1	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0	65535	0	0	0	0.205926	0	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	1	4	11	11	11
+A	4	0x0400000000004000	1678438540.641057	1678438540.641057	0.000000	1	3	eth:ipv4:udp	0c:7a:15:dd:3e:7f	48:73:97:96:38:22	0x0800		192.168.58.75	07	"Private network"	56009	192.168.44.40	07	"Private network"	53	17	0x00	1	0c:7a:15:dd:3e:7f_48:73:97:96:38:22_1	IntelCorp,MY_-	53	domain	1	1	37	69	37	37	37	0	0	0	0	0	0	0	0	-0.3018868	0x0001	65535	0	128	128	0	0x00	0x0c00	0	0x00_0x00000000	0_0	0x00000000_0x00000000	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0x0000	0x0000	0	0	0x00000000	0	1	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.004197	0.004197	0.004197	0.004197	0	0.004197	0	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	4	1	10	10	2.5
+B	4	0x0400000000004001	1678438540.645254	1678438540.645254	0.000000	1	3	eth:ipv4:udp	48:73:97:96:38:22	0c:7a:15:dd:3e:7f	0x0800		192.168.44.40	07	"Private network"	53	192.168.58.75	07	"Private network"	56009	17	0x00	1	48:73:97:96:38:22_0c:7a:15:dd:3e:7f_1	-_IntelCorp,MY	53	domain	1	1	69	37	69	69	69	0	0	0	0	0	0	0	0	0.3018868	0x0001	65535	0	63	63	0	0x00	0x0040	0	0x00_0x00000000	0_0	0x00000000_0x00000000	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0x0000	0x0000	0	0	0x00000000	0	1	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0	65535	0	0	0	0.004197	0	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	1	4	9	9	9
+A	6	0x0400000000004000	1678438540.987686	1678438540.987686	0.000000	1	3	eth:ipv4:udp	0c:7a:15:dd:3e:7f	48:73:97:96:38:22	0x0800		192.168.58.75	07	"Private network"	56010	192.168.44.40	07	"Private network"	53	17	0x00	1	0c:7a:15:dd:3e:7f_48:73:97:96:38:22_1	IntelCorp,MY_-	53	domain	1	1	35	67	35	35	35	0	0	0	0	0	0	0	0	-0.3137255	0x0001	65535	0	128	128	0	0x00	0x0c00	0	0x00_0x00000000	0_0	0x00000000_0x00000000	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0x0000	0x0000	0	0	0x00000000	0	1	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.011171	0.011171	0.011171	0.011171	0	0.011171	0	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	4	1	8	8	2
+B	6	0x0400000000004001	1678438540.998857	1678438540.998857	0.000000	1	3	eth:ipv4:udp	48:73:97:96:38:22	0c:7a:15:dd:3e:7f	0x0800		192.168.44.40	07	"Private network"	53	192.168.58.75	07	"Private network"	56010	17	0x00	1	48:73:97:96:38:22_0c:7a:15:dd:3e:7f_1	-_IntelCorp,MY	53	domain	1	1	67	35	67	67	67	0	0	0	0	0	0	0	0	0.3137255	0x0001	65535	0	63	63	0	0x00	0x0040	0	0x00_0x00000000	0_0	0x00000000_0x00000000	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0x0000	0x0000	0	0	0x00000000	0	1	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0	65535	0	0	0	0.011171	0	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	1	4	7	7	7
+A	8	0x0400000000004000	1678438542.712138	1678438542.712138	0.000000	1	3	eth:ipv4:udp	0c:7a:15:dd:3e:7f	48:73:97:96:38:22	0x0800		192.168.58.75	07	"Private network"	56011	192.168.44.40	07	"Private network"	53	17	0x00	1	0c:7a:15:dd:3e:7f_48:73:97:96:38:22_1	IntelCorp,MY_-	53	domain	1	1	34	86	34	34	34	0	0	0	0	0	0	0	0	-0.4333333	0x0001	65535	0	128	128	0	0x00	0x0c00	0	0x00_0x00000000	0_0	0x00000000_0x00000000	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0x0000	0x0000	0	0	0x00000000	0	1	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.008156	0.008156	0.008156	0.008156	0	0.008156	0	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	4	1	6	6	1.5
+B	8	0x0400000000004001	1678438542.720294	1678438542.720294	0.000000	1	3	eth:ipv4:udp	48:73:97:96:38:22	0c:7a:15:dd:3e:7f	0x0800		192.168.44.40	07	"Private network"	53	192.168.58.75	07	"Private network"	56011	17	0x00	1	48:73:97:96:38:22_0c:7a:15:dd:3e:7f_1	-_IntelCorp,MY	53	domain	1	1	86	34	86	86	86	0	0	0	0	0	0	0	0	0.4333333	0x0001	65535	0	63	63	0	0x00	0x0040	0	0x00_0x00000000	0_0	0x00000000_0x00000000	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0x0000	0x0000	0	0	0x00000000	0	1	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0	65535	0	0	0	0.008156	0	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	1	4	5	5	5
+A	10	0x0400000000004000	1678438542.727009	1678438542.727009	0.000000	1	3	eth:ipv4:udp	0c:7a:15:dd:3e:7f	48:73:97:96:38:22	0x0800		192.168.58.75	07	"Private network"	56012	20.189.79.72	us	"Microsoft Corporation"	123	17	0x00	1	0c:7a:15:dd:3e:7f_48:73:97:96:38:22_1	IntelCorp,MY_-	123	ntp	1	1	48	48	48	48	48	0	0	0	0	0	0	0	0	0	0x0001	65535	0	128	128	0	0x00	0x0c00	0	0x00_0x00000000	0_0	0x00000000_0x00000000	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0x0000	0x0000	0	0	0x00000000	0	1	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.071708	0.071708	0.071708	0.071708	0	0.071708	0	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	4	1	2	2	0.5
+B	10	0x0400000000004001	1678438542.798717	1678438542.798717	0.000000	1	3	eth:ipv4:udp	48:73:97:96:38:22	0c:7a:15:dd:3e:7f	0x0800		20.189.79.72	us	"Microsoft Corporation"	123	192.168.58.75	07	"Private network"	56012	17	0x00	1	48:73:97:96:38:22_0c:7a:15:dd:3e:7f_1	-_IntelCorp,MY	123	ntp	1	1	48	48	48	48	48	0	0	0	0	0	0	0	0	0	0x0001	65535	0	111	111	0	0x00	0x0000	0	0x00_0x00000000	0_0	0x00000000_0x00000000	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0x0000	0x0000	0	0	0x00000000	0	1	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0	65535	0	0	0	0.071708	0	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	1	4	1	1	1
+A	14	0x0400000080004000	1678438543.087950	1678438543.087950	0.000000	1	5	eth:ipv4:udp:udpencap:esp	0c:7a:15:dd:3e:7f	48:73:97:96:38:22	0x0800		192.168.58.75	07	"Private network"	50210	18.156.144.91	us	"Amazon Technologies Inc"	4500	17	0x00	1	0c:7a:15:dd:3e:7f_48:73:97:96:38:22_1	IntelCorp,MY_-	4500	ipsec-nat-t	1	1	1	9	1	1	1	0	0	0	0	0	0	0	0	-0.8	0x0001	65535	0	128	128	0	0x00	0x0c00	0	0x00_0x00000000	0_0	0x00000000_0x00000000	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0x0000	0x0000	0	0	0x00000000	0	1	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.347449	0.347449	0.347449	0.347449	0	0.347449	0	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	3	1	8	2	0.6666667
+B	14	0x0400000080004001	1678438543.435399	1678438543.435399	0.000000	1	5	eth:ipv4:udp:udpencap:esp	48:73:97:96:38:22	0c:7a:15:dd:3e:7f	0x0800		18.156.144.91	us	"Amazon Technologies Inc"	4500	192.168.58.75	07	"Private network"	50210	17	0x00	1	48:73:97:96:38:22_0c:7a:15:dd:3e:7f_1	-_IntelCorp,MY	4500	ipsec-nat-t	1	1	9	1	9	9	9	0	0	0	0	0	0	0	0	0.8	0x0001	65535	0	42	42	0	0x00	0x0040	0	0x00_0x00000000	0_0	0x00000000_0x00000000	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0x0000	0x0000	0	0	0x00000000	0	1	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0	65535	0	0	0	0.347449	0	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	1	3	7	1	1
+A	15	0x0400000000004000	1678438543.087951	1678438543.087951	0.000000	1	3	eth:ipv4:udp	0c:7a:15:dd:3e:7f	48:73:97:96:38:22	0x0800		192.168.58.75	07	"Private network"	50211	18.156.144.91	us	"Amazon Technologies Inc"	51820	17	0x00	1	0c:7a:15:dd:3e:7f_48:73:97:96:38:22_1	IntelCorp,MY_-	51820	unknown	1	1	1	10	1	1	1	0	0	0	0	0	0	0	0	-0.8181818	0x0001	65535	0	128	128	0	0x00	0x0c00	0	0x00_0x00000000	0_0	0x00000000_0x00000000	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0x0000	0x0000	0	0	0x00000000	0	1	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.347448	0.347448	0.347448	0.347448	0	0.347448	0	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	3	1	6	2	0.6666667
+B	15	0x0400000000004001	1678438543.435399	1678438543.435399	0.000000	1	3	eth:ipv4:udp	48:73:97:96:38:22	0c:7a:15:dd:3e:7f	0x0800		18.156.144.91	us	"Amazon Technologies Inc"	51820	192.168.58.75	07	"Private network"	50211	17	0x00	1	48:73:97:96:38:22_0c:7a:15:dd:3e:7f_1	-_IntelCorp,MY	51820	unknown	1	1	10	1	10	10	10	0	0	0	0	0	0	0	0	0.8181818	0x0001	65535	0	42	42	0	0x00	0x0040	0	0x00_0x00000000	0_0	0x00000000_0x00000000	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0x0000	0x0000	0	0	0x00000000	0	1	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0	65535	0	0	0	0.347448	0	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	1	3	5	1	1
+A	16	0x0400000000004000	1678438543.088030	1678438543.088030	0.000000	1	3	eth:ipv4:udp	0c:7a:15:dd:3e:7f	48:73:97:96:38:22	0x0800		192.168.58.75	07	"Private network"	50212	18.156.144.91	us	"Amazon Technologies Inc"	3433	17	0x00	1	0c:7a:15:dd:3e:7f_48:73:97:96:38:22_1	IntelCorp,MY_-	3433	alta-smp	1	1	1	9	1	1	1	0	0	0	0	0	0	0	0	-0.8	0x0001	65535	0	128	128	0	0x00	0x0c00	0	0x00_0x00000000	0_0	0x00000000_0x00000000	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0x0000	0x0000	0	0	0x00000000	0	1	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.347369	0.347369	0.347369	0.347369	0	0.347369	0	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	3	1	4	2	0.6666667
+B	16	0x0400000000004001	1678438543.435399	1678438543.435399	0.000000	1	3	eth:ipv4:udp	48:73:97:96:38:22	0c:7a:15:dd:3e:7f	0x0800		18.156.144.91	us	"Amazon Technologies Inc"	3433	192.168.58.75	07	"Private network"	50212	17	0x00	1	48:73:97:96:38:22_0c:7a:15:dd:3e:7f_1	-_IntelCorp,MY	3433	alta-smp	1	1	9	1	9	9	9	0	0	0	0	0	0	0	0	0.8	0x0001	65535	0	42	42	0	0x00	0x0040	0	0x00_0x00000000	0_0	0x00000000_0x00000000	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0x0000	0x0000	0	0	0x00000000	0	1	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0	65535	0	0	0	0.347369	0	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	1	3	3	1	1
+A	17	0x0400000000004000	1678438543.088059	1678438543.088059	0.000000	1	3	eth:ipv4:udp	0c:7a:15:dd:3e:7f	48:73:97:96:38:22	0x0800		192.168.58.75	07	"Private network"	50213	18.156.144.91	us	"Amazon Technologies Inc"	500	17	0x00	1	0c:7a:15:dd:3e:7f_48:73:97:96:38:22_1	IntelCorp,MY_-	500	isakmp	1	1	1	8	1	1	1	0	0	0	0	0	0	0	0	-0.7777778	0x0001	65535	0	128	128	0	0x00	0x0c00	0	0x00_0x00000000	0_0	0x00000000_0x00000000	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0x0000	0x0000	0	0	0x00000000	0	1	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.34734	0.34734	0.34734	0.34734	0	0.34734	0	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	3	1	2	2	0.6666667
+B	17	0x0400000000004001	1678438543.435399	1678438543.435399	0.000000	1	3	eth:ipv4:udp	48:73:97:96:38:22	0c:7a:15:dd:3e:7f	0x0800		18.156.144.91	us	"Amazon Technologies Inc"	500	192.168.58.75	07	"Private network"	50213	17	0x00	1	48:73:97:96:38:22_0c:7a:15:dd:3e:7f_1	-_IntelCorp,MY	500	isakmp	1	1	8	1	8	8	8	0	0	0	0	0	0	0	0	0.7777778	0x0001	65535	0	42	42	0	0x00	0x0040	0	0x00_0x00000000	0_0	0x00000000_0x00000000	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0x0000	0x0000	0	0	0x00000000	0	1	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0	65535	0	0	0	0.34734	0	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	1	3	1	1	1
+A	30	0x0400000000004000	1678438545.154876	1678438545.154876	0.000000	1	3	eth:ipv4:udp	0c:7a:15:dd:3e:7f	48:73:97:96:38:22	0x0800		192.168.58.75	07	"Private network"	50214	192.168.44.40	07	"Private network"	53	17	0x00	1	0c:7a:15:dd:3e:7f_48:73:97:96:38:22_1	IntelCorp,MY_-	53	domain	1	1	41	73	41	41	41	0	0	0	0	0	0	0	0	-0.2807018	0x0001	65535	0	128	128	0	0x00	0x0c00	0	0x00_0x00000000	0_0	0x00000000_0x00000000	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0x0000	0x0000	0	0	0x00000000	0	1	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.100622	0.100622	0.100622	0.100622	0	0.100622	0	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	2	1	4	4	2
+B	30	0x0400000000004001	1678438545.255498	1678438545.255498	0.000000	1	3	eth:ipv4:udp	48:73:97:96:38:22	0c:7a:15:dd:3e:7f	0x0800		192.168.44.40	07	"Private network"	53	192.168.58.75	07	"Private network"	50214	17	0x00	1	48:73:97:96:38:22_0c:7a:15:dd:3e:7f_1	-_IntelCorp,MY	53	domain	1	1	73	41	73	73	73	0	0	0	0	0	0	0	0	0.2807018	0x0001	65535	0	63	63	0	0x00	0x0040	0	0x00_0x00000000	0_0	0x00000000_0x00000000	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0x0000	0x0000	0	0	0x00000000	0	1	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0	65535	0	0	0	0.100622	0	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	1	2	3	3	3
+A	37	0x0400000000004000	1678438562.153533	1678438562.153533	0.000000	1	3	eth:ipv4:udp	0c:7a:15:dd:3e:7f	48:73:97:96:38:22	0x0800		192.168.58.75	07	"Private network"	50215	192.168.44.40	07	"Private network"	53	17	0x00	1	0c:7a:15:dd:3e:7f_48:73:97:96:38:22_1	IntelCorp,MY_-	53	domain	1	1	43	75	43	43	43	0	0	0	0	0	0	0	0	-0.2711864	0x0001	65535	0	128	128	0	0x00	0x0c00	0	0x00_0x00000000	0_0	0x00000000_0x00000000	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0x0000	0x0000	0	0	0x00000000	0	1	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.067652	0.067652	0.067652	0.067652	0	0.067652	0	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	2	1	2	2	1
+B	37	0x0400000000004001	1678438562.221185	1678438562.221185	0.000000	1	3	eth:ipv4:udp	48:73:97:96:38:22	0c:7a:15:dd:3e:7f	0x0800		192.168.44.40	07	"Private network"	53	192.168.58.75	07	"Private network"	50215	17	0x00	1	48:73:97:96:38:22_0c:7a:15:dd:3e:7f_1	-_IntelCorp,MY	53	domain	1	1	75	43	75	75	75	0	0	0	0	0	0	0	0	0.2711864	0x0001	65535	0	63	63	0	0x00	0x0040	0	0x00_0x00000000	0_0	0x00000000_0x00000000	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0x0000	0x0000	0	0	0x00000000	0	1	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0	65535	0	0	0	0.067652	0	0x00	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	1	2	1	1	1
+A	38	0x0400000000004000	1678438563.217790	1678440659.582582	2096.364792	1	3	eth:ipv4:tcp	0c:7a:15:dd:3e:7f	48:73:97:96:38:22	0x0800		192.168.58.75	07	"Private network"	60610	149.34.250.49	es	"PSINet"	8443	6	0x00	1	0c:7a:15:dd:3e:7f_48:73:97:96:38:22_1994	IntelCorp,MY_-	8443	pcsync-https	1994	2186	330689	609771	0	1300	165.842	313.6865	0	14.62185	1.051335	2.358046	0.9511704	157.744	-0.04593302	-0.2967505	0x4a11	1	1	128	128	0	0x00	0x2c40	0	0x00_0x00000000	0_0	0x00000000_0x00000000	715168985	1915	320815	6	1993	606919	72	13143	64240	523605.1	64240	524288	407	144	408	0	0x001a	0x004c	78	237	0x0000003e	1460	256	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.39586	3.8e-05	14.093	0.5614421	1.491738	0.396135	0	0x02	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	1	1	2	2	2
+B	38	0x0400000000004001	1678438563.613650	1678440659.540542	2095.926892	1	3	eth:ipv4:tcp	48:73:97:96:38:22	0c:7a:15:dd:3e:7f	0x0800		149.34.250.49	es	"PSINet"	8443	192.168.58.75	07	"Private network"	60610	6	0x00	1	48:73:97:96:38:22_0c:7a:15:dd:3e:7f_2186	-_IntelCorp,MY	8443	pcsync-https	2186	1994	609771	330689	0	1300	278.9437	466.9475	0	14.67571	0.9587952	2.281338	1.042975	290.9314	0.04593302	0.2967505	0x4a11	1	65150	45	45	0	0x00	0x2044	0	0x00_0x00000000	0_0	0x00000000_0x00000000	2675087647	2102	732757	7	2186	312805	42	17699	65535	522832	65535	522832	8	177	9	0	0x021a	0x02cc	74	225	0x0000003e	1300	16	0x0000	0x00	0	0x00	0	0	0	0.000000	0.000000	0.000275	3.7e-05	14.66692	0.5466043	1.868303	1.108046	2.390782	0x02	0x00	0	0x00000000_0x00000000_0x0000	0x00000000	0	0	1	1	1	1	1
--- a/wcx-抓包-用于模型复现/sufshark_t2.txt_headers.txt
+++ b/wcx-抓包-用于模型复现/sufshark_t2.txt_headers.txt
@@ -0,0 +1,128 @@
+# Date: 1678503926.012904 sec (Sat 11 Mar 2023 11:05:26 CST)
+# Tranalyzer 0.8.14 (Anteater), Tarantula.
+# Core configuration: L2, IPv4, IPv6
+# SensorID: 666
+# PID: 32097
+# Command line: /Users/fangxiaoyu/tranalyzer2-0.8.14/tranalyzer2/build/tranalyzer -r /Users/fangxiaoyu/Desktop/VPN及其流量识别研究/抓包分析/wcx-抓包-用于模型复现/sufshark_openvpn_tcp.pcap -w /Users/fangxiaoyu/Desktop/VPN及其流量识别研究/抓包分析/wcx-抓包-用于模型复现/sufshark_t2.txt
+# HW info: fangxiaoyudeMacBook-Pro.local;Darwin;22.3.0;Darwin Kernel Version 22.3.0: Mon Jan 30 20:38:37 PST 2023; root:xnu-8792.81.3~2/RELEASE_ARM64_T6000;arm64
+#
+# Plugins loaded:
+#   01: protoStats, version 0.8.14
+#   02: basicFlow, version 0.8.14
+#   03: macRecorder, version 0.8.14
+#   04: portClassifier, version 0.8.14
+#   05: basicStats, version 0.8.14
+#   06: tcpFlags, version 0.8.14
+#   07: tcpStates, version 0.8.14
+#   08: icmpDecode, version 0.8.14
+#   09: connStat, version 0.8.14
+#   10: txtSink, version 0.8.14
+#
+# Col No.	Type	Name	Description
+1	C	dir	Flow direction
+2	U64	flowInd	Flow index
+3	H64	flowStat	Flow status and warnings
+4	U64.U32	timeFirst	Date time of first packet
+5	U64.U32	timeLast	Date time of last packet
+6	U64.U32	duration	Flow duration
+7	U8	numHdrDesc	Number of different headers descriptions
+8	U16:R	numHdrs	Number of headers (depth) in hdrDesc
+9	SC:R	hdrDesc	Headers description
+10	MAC:R	srcMac	Mac source
+11	MAC:R	dstMac	Mac destination
+12	H16	ethType	Ethernet type
+13	U16:R	ethVlanID	VLAN IDs
+14	IPX	srcIP	Source IP address
+15	SC	srcIPCC	Source IP country
+16	S	srcIPOrg	Source IP organisation
+17	U16	srcPort	Source port
+18	IPX	dstIP	Destination IP address
+19	SC	dstIPCC	Destination IP country
+20	S	dstIPOrg	Destination IP organisation
+21	U16	dstPort	Destination port
+22	U8	l4Proto	Layer 4 protocol
+23	H8	macStat	macRecorder status
+24	U32	macPairs	Number of distinct source/destination MAC addresses pairs
+25	MAC_MAC_U64:R	srcMac_dstMac_numP	Source/destination MAC address, number of packets of MAC address combination
+26	SC_SC:R	srcMacLbl_dstMacLbl	Source/destination MAC label
+27	U16	dstPortClassN	Port based classification of the destination port number
+28	SC	dstPortClass	Port based classification of the destination port name
+29	U64	numPktsSnt	Number of transmitted packets
+30	U64	numPktsRcvd	Number of received packets
+31	U64	numBytesSnt	Number of transmitted bytes
+32	U64	numBytesRcvd	Number of received bytes
+33	U16	minPktSz	Minimum layer 3 packet size
+34	U16	maxPktSz	Maximum layer 3 packet size
+35	F	avePktSize	Average layer 3 packet size
+36	F	stdPktSize	Standard deviation layer 3 packet size
+37	F	minIAT	Minimum IAT
+38	F	maxIAT	Maximum IAT
+39	F	aveIAT	Average IAT
+40	F	stdIAT	Standard deviation IAT
+41	F	pktps	Sent packets per second
+42	F	bytps	Sent bytes per second
+43	F	pktAsm	Packet stream asymmetry
+44	F	bytAsm	Byte stream asymmetry
+45	H16	tcpFStat	tcpFlags status
+46	U16	ipMindIPID	IP minimum delta IP ID
+47	U16	ipMaxdIPID	IP maximum delta IP ID
+48	U8	ipMinTTL	IP minimum TTL
+49	U8	ipMaxTTL	IP maximum TTL
+50	U8	ipTTLChg	IP TTL change count
+51	H8	ipToS	IP Type of Service hex
+52	H16	ipFlags	IP aggregated flags
+53	U16	ipOptCnt	IP options count
+54	H8_H32	ipOptCpCl_Num	IP aggregated options, copy-class and number
+55	U16_U16	ip6OptCntHH_D	IPv6 Hop-by-Hop destination option counts
+56	H32_H32	ip6OptHH_D	IPv6 aggregated Hop-by-Hop destination options
+57	U32	tcpISeqN	TCP initial sequence number
+58	U16	tcpPSeqCnt	TCP packet seq count
+59	U64	tcpSeqSntBytes	TCP sent seq diff bytes
+60	U16	tcpSeqFaultCnt	TCP sequence number fault count
+61	U16	tcpPAckCnt	TCP packet ACK count
+62	U64	tcpFlwLssAckRcvdBytes	TCP flawless ACK received bytes
+63	U16	tcpAckFaultCnt	TCP ACK number fault count
+64	U32	tcpBFlgtMx	TCP Bytes in Flight MAX
+65	U32	tcpInitWinSz	TCP initial effective window size
+66	F	tcpAveWinSz	TCP average effective window size
+67	U32	tcpMinWinSz	TCP minimum effective window size
+68	U32	tcpMaxWinSz	TCP maximum effective window size
+69	U16	tcpWinSzDwnCnt	TCP effective window size change down count
+70	U16	tcpWinSzUpCnt	TCP effective window size change up count
+71	U16	tcpWinSzChgDirCnt	TCP effective window size direction change count
+72	F	tcpWinSzThRt	TCP packet count ratio below window size WINMIN threshold
+73	H16	tcpFlags	TCP aggregated protocol flags (FINACK, SYNACK, RSTACK, CWR, ECE, URG, ACK, PSH, RST, SYN, FIN)
+74	H16	tcpAnomaly	TCP aggregated header anomaly flags
+75	U16	tcpOptPktCnt	TCP options packet count
+76	U16	tcpOptCnt	TCP options count
+77	H32	tcpOptions	TCP aggregated options
+78	U16	tcpMSS	TCP maximum segment size
+79	U16	tcpWS	TCP window scale
+80	H16	tcpMPTBF	TCP MPTCP type bitfield
+81	H8	tcpMPF	TCP MPTCP flags
+82	U8	tcpMPAID	TCP MPTCP address ID
+83	H8	tcpMPDSSF	TCP MPTCP DSS flags
+84	U32	tcpTmS	TCP time stamp
+85	U32	tcpTmER	TCP time echo reply
+86	F	tcpEcI	TCP estimated counter increment
+87	D	tcpUtm	TCP estimated up time
+88	U64.U32	tcpBtm	TCP estimated boot time
+89	F	tcpSSASAATrip	TCP trip time (A: SYN, SYN-ACK, B: SYN-ACK, ACK)
+90	F	tcpRTTAckTripMin	TCP ACK trip min
+91	F	tcpRTTAckTripMax	TCP ACK trip max
+92	F	tcpRTTAckTripAve	TCP ACK trip average
+93	F	tcpRTTAckTripJitAve	TCP ACK trip jitter average
+94	F	tcpRTTSseqAA	TCP round trip time (A: SYN, SYN-ACK, ACK, B: ACK-ACK)
+95	F	tcpRTTAckJitAve	TCP ACK round trip average jitter
+96	H8	tcpStatesAFlags	TCP state machine anomalies
+97	H8	icmpStat	ICMP Status
+98	U8	icmpTCcnt	ICMP type code count
+99	H32_H32_H16	icmpBFTypH_TypL_Code	ICMP Aggregated type H (>128), L (<32) & code bit field
+100	H32	icmpTmGtw	ICMP time/gateway
+101	F	icmpEchoSuccRatio	ICMP Echo reply/request success ratio
+102	U64	icmpPFindex	ICMP parent flowIndex
+103	U32	connSip	Number of unique source IPs
+104	U32	connDip	Number of unique destination IPs
+105	U32	connSipDip	Number of connections between source and destination IP
+106	U32	connSipDprt	Number of connections between source IP and destination port
+107	F	connF	The f number: connSipDprt / connSip [EXPERIMENTAL]
--- a/wcx-抓包-用于模型复现/sufshark_t2.txt_icmpStats.txt
+++ b/wcx-抓包-用于模型复现/sufshark_t2.txt_icmpStats.txt
@@ -0,0 +1 @@
+Total number of ICMP packets: 0 [0.00%]
--- a/wcx-抓包-用于模型复现/sufshark_t2.txt_protocols.txt
+++ b/wcx-抓包-用于模型复现/sufshark_t2.txt_protocols.txt
@@ -0,0 +1,30 @@
+# Total packets: 4790 (4.79 K)
+# Total bytes: 1429275 (1.43 M)
+# L2/3 Protocol	                       Packets	                         Bytes	Description
+0x0800	                4790 [100.00%]	             1429275 [100.00%]	Internet Protocol version 4 (IPv4)
+
+
+# Total IPv4 packets: 4790 (4.79 K) [100.00%]
+# Total IPv6 packets: 0 [0.00%]
+# L4 Protocol	                       Packets	                         Bytes	Description
+  6	                4768 [ 99.54%]	             1427519 [ 99.88%]	Transmission Control Protocol (TCP)
+ 17	                  22 [  0.46%]	                1756 [  0.12%]	User Datagram Protocol (UDP)
+
+
+# Total TCP packets: 4768 (4.77 K) [99.54%]
+# Total TCP bytes: 1427519 (1.43 M) [99.88%]
+# TCP Port	                       Packets	                         Bytes	Description
+   80	                   7 [  0.15%]	                 421 [  0.03%]	World Wide Web HTTP
+  443	                 574 [ 12.04%]	              253311 [ 17.74%]	HTTP protocol over TLS/SSL
+ 8443	                4187 [ 87.81%]	             1173787 [ 82.23%]	PCsync HTTPS
+
+
+# Total UDP packets: 22 [0.46%]
+# Total UDP bytes: 1756 (1.76 K) [0.12%]
+# UDP Port	                       Packets	                         Bytes	Description
+   53	                  12 [ 54.55%]	                1164 [ 66.29%]	Domain Name Server (DNS)
+  123	                   2 [  9.09%]	                 180 [ 10.25%]	Network Time Protocol
+  500	                   2 [  9.09%]	                 103 [  5.87%]	isakmp
+ 3433	                   2 [  9.09%]	                 103 [  5.87%]	Altaworks Service Management Platform
+ 4500	                   2 [  9.09%]	                 103 [  5.87%]	IPsec NAT-Traversal
+51820	                   2 [  9.09%]	                 103 [  5.87%]	
--- a/wcx-抓包-用于模型复现/surkshark_openvpn_udp.pcap
+++ b/wcx-抓包-用于模型复现/surkshark_openvpn_udp.pcap
--- a/wcx-抓包-用于模型复现/torguard_openvpn_udp.pcap
+++ b/wcx-抓包-用于模型复现/torguard_openvpn_udp.pcap
				`@@ -0,0 +1 @@`
				`src_ip,dst_ip,src_port,dst_port,proto,num_packets,bytes,duration,timestamp_start,timestamp_end,flags`