49 lines
1.6 KiB
Python
49 lines
1.6 KiB
Python
# Name:fang xiaoyu
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# Time: 2023/3/10 09:17
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import os
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import json
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import pandas as pd
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from sklearn.neighbors import KNeighborsClassifier
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from sklearn.model_selection import train_test_split
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# 定义Tranalyzer2命令和特征提取命令
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tranalyzer_cmd = "t2 -r {} -w {} -t"
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feature_cmd = "t2 -r {} --bidir --tcp --protoid --statsonly --export json"
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# 定义pcap文件路径和输出文件路径
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pcap_file = "20230309_fxy_psiphon_operation.pcapng"
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binetflow_file = "capture.binetflow"
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# 转换pcap文件为binetflow格式
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os.system(tranalyzer_cmd.format(pcap_file, binetflow_file))
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# 提取特征并保存到json文件中
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os.system(feature_cmd.format(binetflow_file) + " > features.json")
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# 读取json文件中的特征数据并转换为DataFrame格式
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with open("features.json", "r") as f:
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data = json.load(f)
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df = pd.DataFrame(data)
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# 将标签列转换为数值类型（0或1）
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df["label"] = df["label"].apply(lambda x: 0 if x == "normal" else 1)
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# 将数据集划分为训练集和测试集
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X_train, X_test, y_train, y_test = train_test_split(df.drop("label", axis=1), df["label"], test_size=0.2)
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# 创建KNN分类器对象，设置邻居数量为5
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knn_model = KNeighborsClassifier(n_neighbors=5)
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# 训练模型并预测测试集结果
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knn_model.fit(X_train, y_train)
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y_pred = knn_model.predict(X_test)
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# 输出准确率和混淆矩阵等评估指标
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from sklearn.metrics import accuracy_score, confusion_matrix
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print("Accuracy:", accuracy_score(y_test, y_pred))
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print("Confusion Matrix:\n", confusion_matrix(y_test, y_pred))
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#$ tranalyzer2 -r sample.flow -w sample.features -t templates/plugins/ipfix-allfields.txt
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