223 lines
7.6 KiB
Python
223 lines
7.6 KiB
Python
import pandas as pd
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from sklearn.model_selection import StratifiedKFold
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from sklearn.naive_bayes import GaussianNB
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from sklearn.tree import DecisionTreeClassifier
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from sklearn.svm import SVC
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.metrics import accuracy_score, recall_score, f1_score, precision_score, confusion_matrix
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import sys
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import _pickle as pkl
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import numpy as np
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import warnings
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warnings.filterwarnings("ignore")
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features_name = [
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"Flow Duration",
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"Total Fwd Packet",
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"Total Bwd packets",
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"Total Length of Fwd Packet",
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"Total Length of Bwd Packet",
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"Fwd Packet Length Max",
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"Fwd Packet Length Min",
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"Fwd Packet Length Mean",
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"Fwd Packet Length Std",
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"Bwd Packet Length Max",
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"Bwd Packet Length Min",
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"Bwd Packet Length Mean",
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"Bwd Packet Length Std",
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"Flow Bytes/s",
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"Flow Packets/s",
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"Flow IAT Mean",
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"Flow IAT Std",
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"Flow IAT Max",
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"Flow IAT Min",
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"Fwd IAT Total",
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"Fwd IAT Mean",
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"Fwd IAT Std",
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"Fwd IAT Max",
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"Fwd IAT Min",
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"Bwd IAT Total",
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"Bwd IAT Mean",
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"Bwd IAT Std",
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"Bwd IAT Max",
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"Bwd IAT Min",
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"Fwd PSH Flags",
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"Bwd PSH Flags",
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"Fwd URG Flags",
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"Bwd URG Flags",
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"Fwd Header Length",
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"Bwd Header Length",
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"Fwd Packets/s",
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"Bwd Packets/s",
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"Packet Length Min",
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"Packet Length Max",
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"Packet Length Mean",
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"Packet Length Std",
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"Packet Length Variance",
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"FIN Flag Count",
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"SYN Flag Count",
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"RST Flag Count",
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"PSH Flag Count",
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"ACK Flag Count",
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"URG Flag Count",
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"CWR Flag Count",
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"ECE Flag Count",
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"Down/Up Ratio",
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"Average Packet Size",
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"Fwd Segment Size Avg",
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"Bwd Segment Size Avg",
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"Fwd Bytes/Bulk Avg",
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"Fwd Packet/Bulk Avg",
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"Fwd Bulk Rate Avg",
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"Bwd Bytes/Bulk Avg",
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"Bwd Packet/Bulk Avg",
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"Bwd Bulk Rate Avg",
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"Subflow Fwd Packets",
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"Subflow Fwd Bytes",
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"Subflow Bwd Packets",
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"Subflow Bwd Bytes",
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"FWD Init Win Bytes",
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"Bwd Init Win Bytes",
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"Fwd Act Data Pkts",
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"Fwd Seg Size Min",
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"Active Mean",
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"Active Std",
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"Active Max",
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"Active Min",
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"Idle Mean",
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"Idle Std",
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"Idle Max",
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"Idle Min",
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]
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def print_important_feature(sort_index, num=10):
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print("top important feature is:")
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for index in sort_index[:num]:
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print(features_name[index])
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def random_forest(train, test, test_ow="ndarray"):
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X = train.features.tolist()
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Y = train.label.tolist()
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test_X = test.features.tolist()
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# print(len(X), len(Y))
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# print(len(X[0]))
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nb = RandomForestClassifier()
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nb.fit(X, Y)
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# importance = nb.feature_importances_
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# sort_index = np.flipud(importance.argsort())
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# print_important_feature(sort_index)
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pred_ret = nb.predict(test_X)
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if not isinstance(test_ow, str):
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ow_X = test_ow.features.tolist()
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return pred_ret, nb.predict(ow_X)
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else:
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return pred_ret
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def naive_bayesian(train, test, test_ow="ndarray"):
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X = train.features.tolist()
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Y = train.label.tolist()
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test_X = test.features.tolist()
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# print(len(X), len(Y))
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# print(len(X[0]))
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rf = GaussianNB()
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rf.fit(X, Y)
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# importance = rf.feature_importances_
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# sort_index = np.flipud(importance.argsort())
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# print_important_feature(sort_index)
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pred_ret = rf.predict(test_X)
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if not isinstance(test_ow, str):
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ow_X = test_ow.features.tolist()
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return pred_ret, rf.predict(ow_X)
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else:
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return pred_ret
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def decision_tree_classifier(train, test, test_ow="ndarray"):
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X = train.features.tolist()
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Y = train.label.tolist()
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test_X = test.features.tolist()
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# print(len(X), len(Y))
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# print(len(X[0]))
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dt = DecisionTreeClassifier()
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dt.fit(X, Y)
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# importance = rf.feature_importances_
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# sort_index = np.flipud(importance.argsort())
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# print_important_feature(sort_index)
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pred_ret = dt.predict(test_X)
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if not isinstance(test_ow, str):
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ow_X = test_ow.features.tolist()
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return pred_ret, dt.predict(ow_X)
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else:
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return pred_ret
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def svm_classifier(train, test, test_ow="ndarray"):
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X = train.features.tolist()
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Y = train.label.tolist()
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test_X = test.features.tolist()
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# print(len(X), len(Y))
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# print(len(X[0]))
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svm = SVC()
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svm.fit(X, Y)
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# importance = rf.feature_importances_
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# sort_index = np.flipud(importance.argsort())
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# print_important_feature(sort_index)
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pred_ret = svm.predict(test_X)
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if not isinstance(test_ow, str):
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ow_X = test_ow.features.tolist()
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return pred_ret, svm.predict(ow_X)
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else:
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return pred_ret
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if __name__ == "__main__":
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kf = StratifiedKFold(n_splits=5, shuffle=True)
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# for file in ["./result/web_features.pkl", "./result/chat_features.pkl", "./result/email_features.pkl",
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# "./result/voip_features.pkl", "./result/file_features.pkl"]:
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doh_dataset = pkl.load(open("./result/doh_features.pkl", "rb"))
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web_dataset = pkl.load(open("./result/web_features.pkl", "rb"))
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chat_features = pkl.load(open("./result/chat_features.pkl", "rb"))
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email_features = pkl.load(open("./result/email_features.pkl", "rb"))
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voip_features = pkl.load(open("./result/voip_features.pkl", "rb"))
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file_features = pkl.load(open("./result/file_features.pkl", "rb"))
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doh_dataset['label'] = doh_dataset['label'].map(lambda x: 0)
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web_dataset['label'] = web_dataset['label'].map(lambda x: 1)
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chat_features['label'] = chat_features['label'].map(lambda x: 1)
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email_features['label'] = email_features['label'].map(lambda x: 1)
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voip_features['label'] = voip_features['label'].map(lambda x: 1)
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file_features['label'] = file_features['label'].map(lambda x: 1)
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# doh_dataset = doh_dataset.sample(min(len(web_dataset), len(doh_dataset) * 1))
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# web_dataset = web_dataset.sample(min(len(web_dataset), len(doh_dataset) * 1))
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# print("数据集组成如下：")
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# print(f"封闭数据集中正负样本比例为1:{len(web_dataset) // len(doh_dataset)}，"
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# f"正样本数量为{len(doh_dataset)},负样本数量为{len(web_dataset)}")
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print("load data suc!")
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# cw_dataset = pd.concat([web_dataset, doh_dataset])
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# ow_dataset = pd.concat([ow_web_dataset, ow_doh_dataset])
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classify = random_forest
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for negativeSet in [web_dataset, chat_features, email_features, voip_features, file_features]:
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for testSet in [web_dataset, chat_features, email_features, voip_features, file_features]:
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if len(negativeSet) == len(testSet):
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continue
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positiveSet = doh_dataset.sample(min(len(negativeSet), len(doh_dataset)))
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negativeSet = negativeSet.sample(min(len(negativeSet), len(doh_dataset)))
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trainSet = pd.concat([positiveSet, negativeSet])
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predict_results = classify(trainSet, testSet)
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gt_Y = testSet.label.tolist()
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precision = precision_score(gt_Y, predict_results, pos_label=1, average="binary")
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recall = recall_score(gt_Y, predict_results, pos_label=1, average="binary")
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f1 = f1_score(gt_Y, predict_results, pos_label=1, average="binary")
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acc = accuracy_score(gt_Y, predict_results)
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print(confusion_matrix(gt_Y, predict_results))
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print("封闭测试集准确率: ", precision, end="\t")
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print("封闭测试集召回率: ", recall, end="\t")
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print("封闭测试集f1值: ", f1, end="\t")
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print("封闭测试集acc: ", acc)
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