264 lines
8.4 KiB
Python
264 lines
8.4 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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import pandas as pd
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import numpy as np
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import xgboost as xgb
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
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import matplotlib.pyplot as plt
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import seaborn as sns
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import os
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import joblib
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def load_data(malware_csv, whitelist_csv):
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"""
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加载恶意软件和白名单CSV文件
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"""
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print(f"加载恶意软件数据: {malware_csv}")
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# 预处理：先获取CSV的列数
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# 读取第一行以确定正确的列数
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try:
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header = pd.read_csv(malware_csv, nrows=1)
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expected_columns = len(header.columns)
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print(f"预期列数: {expected_columns}")
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# 使用自定义函数读取CSV，处理字段不足的行
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malware_df = pd.read_csv(
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malware_csv,
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header=0,
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low_memory=False,
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on_bad_lines='skip', # 跳过无法解析的行
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dtype=float, # 将所有数据列转为浮点型
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converters={0: str} # 第一列为文件路径，保持为字符串类型
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)
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# 检查列数是否不足，如果不足则填充0
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actual_columns = len(malware_df.columns)
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if actual_columns < expected_columns:
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for i in range(actual_columns, expected_columns):
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col_name = f"col_{i}"
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malware_df[col_name] = 0.0
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print(f"成功读取恶意软件数据，形状: {malware_df.shape}")
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except Exception as e:
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print(f"读取恶意软件数据时出错: {e}")
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return None, None
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malware_df['label'] = 1 # 恶意软件标签为1
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print(f"加载白名单数据: {whitelist_csv}")
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try:
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# 同样处理白名单数据
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whitelist_df = pd.read_csv(
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whitelist_csv,
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header=0,
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low_memory=False,
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on_bad_lines='skip',
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dtype=float,
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converters={0: str}
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)
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# 确保列数与恶意软件数据一致
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whitelist_cols = len(whitelist_df.columns)
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malware_cols = len(malware_df.columns) - 1 # 减去标签列
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if whitelist_cols < malware_cols:
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for i in range(whitelist_cols, malware_cols):
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col_name = f"col_{i}"
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whitelist_df[col_name] = 0.0
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print(f"成功读取白名单数据，形状: {whitelist_df.shape}")
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except Exception as e:
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print(f"读取白名单数据时出错: {e}")
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return None, None
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whitelist_df['label'] = 0 # 白名单软件标签为0
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# 确保两个DataFrame的列完全一致（除了可能的文件路径差异）
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malware_features = set(malware_df.columns)
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whitelist_features = set(whitelist_df.columns)
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# 找出不同的列
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malware_only = malware_features - whitelist_features
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whitelist_only = whitelist_features - malware_features
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# 为缺少的列添加0值
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for col in malware_only:
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if col != 'label':
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whitelist_df[col] = 0.0
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for col in whitelist_only:
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if col != 'label':
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malware_df[col] = 0.0
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# 合并数据
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combined_df = pd.concat([malware_df, whitelist_df], ignore_index=True, sort=False)
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# 第一列通常是文件路径，需要将其移除
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# 先保存文件路径以便后续参考
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file_paths = combined_df.iloc[:, 0].tolist()
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features = combined_df.iloc[:, 1:-1] # 除去第一列(文件路径)和最后一列(标签)
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labels = combined_df['label']
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print(f"数据加载完成: {len(malware_df)} 个恶意样本, {len(whitelist_df)} 个白名单样本")
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print(f"特征维度: {features.shape}")
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return features, labels
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def train_xgboost_model(X_train, y_train, X_test, y_test):
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"""
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训练XGBoost模型
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"""
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print("开始训练XGBoost模型...")
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# 处理数据中可能存在的NaN值
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print("检查并填充缺失值...")
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X_train = X_train.fillna(0)
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X_test = X_test.fillna(0)
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# 检查是否还有无限值，并将其替换为0
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X_train = X_train.replace([np.inf, -np.inf], 0)
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X_test = X_test.replace([np.inf, -np.inf], 0)
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print(f"处理后的训练数据形状: {X_train.shape}")
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print(f"处理后的测试数据形状: {X_test.shape}")
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# 设置XGBoost参数
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params = {
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'max_depth': 6, # 树的最大深度
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'learning_rate': 0.1, # 学习率
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'n_estimators': 100, # 树的数量
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'objective': 'binary:logistic', # 二分类问题
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'eval_metric': 'logloss', # 评估指标
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'subsample': 0.8, # 样本采样率
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'colsample_bytree': 0.8, # 特征采样率
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'random_state': 42 # 随机种子
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}
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# 创建XGBoost分类器
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model = xgb.XGBClassifier(**params)
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# 训练模型
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model.fit(
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X_train, y_train,
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eval_set=[(X_train, y_train), (X_test, y_test)],
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early_stopping_rounds=10,
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verbose=True
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)
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print("模型训练完成！")
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return model
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def evaluate_model(model, X_test, y_test):
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"""
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评估模型性能
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"""
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print("评估模型性能...")
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# 在测试集上进行预测
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y_pred = model.predict(X_test)
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# 计算准确率
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accuracy = accuracy_score(y_test, y_pred)
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print(f"准确率: {accuracy:.4f}")
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# 打印分类报告
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print("\n分类报告:")
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print(classification_report(y_test, y_pred, target_names=['白名单', '恶意软件']))
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# 打印混淆矩阵
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cm = confusion_matrix(y_test, y_pred)
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plt.figure(figsize=(8, 6))
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sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',
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xticklabels=['白名单', '恶意软件'],
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yticklabels=['白名单', '恶意软件'])
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plt.xlabel('预测')
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plt.ylabel('实际')
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plt.title('混淆矩阵')
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plt.savefig('confusion_matrix.png')
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plt.close()
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# 显示特征重要性
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plt.figure(figsize=(12, 8))
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xgb.plot_importance(model, max_num_features=20)
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plt.title('特征重要性')
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plt.savefig('feature_importance.png')
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plt.close()
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return accuracy
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def save_model(model, output_path='xgboost_malware_detector.model'):
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"""
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保存模型到文件
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"""
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print(f"保存模型到 {output_path}")
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joblib.dump(model, output_path)
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print("模型保存完成！")
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def main():
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"""
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主函数：加载数据，训练模型，评估结果，保存模型
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"""
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try:
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print("开始恶意软件检测模型训练...")
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# 设置文件路径
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malware_csv = 'data/malware_features.csv'
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whitelist_csv = 'data/whitelist_features.csv'
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# 检查文件是否存在
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if not os.path.exists(malware_csv):
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print(f"错误: 找不到恶意软件特征文件 {malware_csv}")
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return
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if not os.path.exists(whitelist_csv):
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print(f"错误: 找不到白名单特征文件 {whitelist_csv}")
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return
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# 加载数据
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X, y = load_data(malware_csv, whitelist_csv)
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if X is None or y is None:
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print("数据加载失败，终止训练")
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return
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print(f"数据集加载完成，共 {len(X)} 个样本")
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# 数据划分
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try:
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X_train, X_test, y_train, y_test = train_test_split(
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X, y, test_size=0.2, random_state=42, stratify=y)
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print(f"训练集: {len(X_train)} 样本，测试集: {len(X_test)} 样本")
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except Exception as e:
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print(f"数据划分出错: {e}")
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return
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# 训练模型
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try:
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model = train_xgboost_model(X_train, y_train, X_test, y_test)
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except Exception as e:
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print(f"模型训练出错: {e}")
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return
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# 评估模型
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try:
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evaluate_model(model, X_test, y_test)
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except Exception as e:
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print(f"模型评估出错: {e}")
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# 保存模型
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try:
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save_model(model)
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print("模型训练和评估完成！")
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except Exception as e:
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print(f"模型保存出错: {e}")
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except Exception as e:
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print(f"训练过程中发生未预期错误: {e}")
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if __name__ == "__main__":
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main() |