five modes duplication

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