44 lines
1.2 KiB
Python
44 lines
1.2 KiB
Python
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# Name:fang xiaoyu
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# Time: 2023/3/11 22:30
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import pandas as pd
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from sklearn.model_selection import train_test_split
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from sklearn.svm import SVC
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from sklearn.metrics import classification_report
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# 读取CSV文件
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data = pd.read_csv('sufshark_openvpn_tcp+youdao_header.csv')
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# 将类别转换为数字标签
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# le = preprocessing.LabelEncoder()
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# data['label'] = le.fit_transform(data['label'])
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data["class1"] = data["class1"].replace({"VPN": 1, "Non-VPN": 0})
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# 分离特征和类别
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X = data.iloc[:, :-1]
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y = data.iloc[:, -1]
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# # 分离特征和标签
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# X = data.drop('label', axis=1)
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# y = data['label']
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# 划分训练集和测试集
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
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# 创建SVM模型
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svm_model = SVC()
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# 在训练集上训练模型
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svm_model.fit(X_train, y_train)
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# 在测试集上评估模型
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predictions = svm_model.predict(X_test)
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print(classification_report(y_test, predictions))
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# precision recall f1-score support
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#
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# 0 0.59 0.42 0.49 1720
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# 1 0.61 0.76 0.67 2032
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#
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# accuracy 0.60 3752
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# macro avg 0.60 0.59 0.58 3752
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# weighted avg 0.60 0.60 0.59 3752
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