怎样使用Python实现基于机器学习的入侵检测算法的训练和评估

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import precision_score, recall_score, roc_curve, auc

# 数据预处理
data = pd.read_csv('intrusion_detection.csv')

# 将种别变量进行编码
le = LabelEncoder()
data['protocol_type'] = le.fit_transform(data['protocol_type'])

# 将数据集分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(data.drop('target', axis=1), data['target'], test_size=0.2, random_state=42)

# 模型训练
rf = RandomForestClassifier(n_estimators=100, random_state=42)
rf.fit(X_train, y_train)

# 模型评估
y_pred = rf.predict(X_test)
precision = precision_score(y_test, y_pred)
recall = recall_score(y_test, y_pred)
fpr, tpr, _ = roc_curve(y_test, y_pred)
roc_auc = auc(fpr, tpr)

print('Precision:', precision)
print('Recall:', recall)
print('AUC:', roc_auc)