Python 代码实现高性能异构漏洞检测系统
数据预处理模块：
数据加载 数据清洗 数据格式化
import pandas as pd
import numpy as np
def load_data(file_path):
return pd.read_csv(file_path)
def clean_data(data):
data = data.dropna() # Remove missing values
data = data.drop_duplicates() # Remove duplicate entries
return data
def format_data(data):
# Example of data formatting, e.g., encoding categorical variables
data['category'] = data['category'].astype('category').cat.codes
return data
特征提取模块：
静态特征提取 动态特征提取
import hashlib
def static_feature_extraction(data):
# Example: Extract hash of the file as a static feature
data['file_hash'] = data['file_path'].apply(lambda x: hashlib.md5(open(x,'rb').read()).hexdigest())
return data
def dynamic_feature_extraction(data):
# Example: Extract runtime behavior (e.g., system calls)
# This is a placeholder for actual dynamic analysis which can be complex
data['dynamic_feature'] = data['file_path'].apply(lambda x: simulate_dynamic_analysis(x))
return data
def simulate_dynamic_analysis(file_path):
# Placeholder for dynamic analysis
return np.random.rand()
模型训练模块：
模型选择 模型训练 模型评估
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report
def train_model(features, labels):
X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2, random_state=42)
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
print(classification_report(y_test, predictions))
return model
漏洞检测模块：
输入数据检测 漏洞分类 报告生成
def detect_vulnerabilities(model, new_data):
features = new_data[['static_feature', 'dynamic_feature']]
predictions = model.predict(features)
new_data['vulnerability'] = predictions
return new_data
def classify_vulnerability(data):
data['vuln_classification'] = data['vulnerability'].apply(lambda x: 'Critical' if x == 1 else 'Non-Critical')
return data
def generate_report(data):
report = data[['file_path', 'vuln_classification']]
report.to_csv('vulnerability_report.csv', index=False)
print("Report generated: vulnerability_report.csv")
结果分析模块：
结果汇总 结果可视化 报告生成
import matplotlib.pyplot as plt
def summarize_results(data):
summary = data['vuln_classification'].value_counts()
print(summary)
return summary
def visualize_results(summary):
summary.plot(kind='bar')
plt.xlabel('Vulnerability Classification')
plt.ylabel('Number of Files')
plt.title('Vulnerability Detection Summary')
plt.show()
def generate_summary_report(data):
summary = summarize_results(data)
visualize_results(summary)
summary.to_csv('summary_report.csv', index=False)
print("Summary report generated: summary_report.csv")
综合脚本
def main():
# Load and preprocess data
data = load_data('input_files.csv')
data = clean_data(data)
data = format_data(data)
# Feature extraction
data = static_feature_extraction(data)
data = dynamic_feature_extraction(data)
# Model training
features = data[['static_feature', 'dynamic_feature']]
labels = data['vulnerability_label']
model = train_model(features, labels)
# Vulnerability detection
new_data = load_data('new_files.csv')
new_data = clean_data(new_data)
new_data = format_data(new_data)
new_data = static_feature_extraction(new_data)
new_data = dynamic_feature_extraction(new_data)
detected_data = detect_vulnerabilities(model, new_data)
detected_data = classify_vulnerability(detected_data)
generate_report(detected_data)
# Results analysis
generate_summary_report(detected_data)
if __name__ == "__main__":
main()
这个脚本假设你有两个数据文件：input_files.csv（用于训练模型的输入文件）和 new_files.csv（用于检测的新文件）。脚本会加载数据、预处理、提取特征、训练模型、检测漏洞并生成报告。你可以根据需要调整这些步骤中的细节和逻辑。
C++ 代码实现高性能异构漏洞检测系统
数据预处理模块：
数据加载 数据清洗 数据格式化
#include <iostream>
#include <fstream>
#include <sstream>
#include <vector>
#include <string>
#include <algorithm>
std::vector<std::vector<std::string>> loadData(const std::string& filePath) {
std::vector<std::vector<std::string>> data;
std::ifstream file(filePath);
std::string line;
while (std::getline(file, line)) {
std::vector<std::string> row;
std::stringstream ss(line);
std::string value;
while (std::getline(ss, value, ',')) {
row.push_back(value);
}
data.push_back(row);
}
return data;
}
std::vector<std::vector<std::string>> cleanData(std::vector<std::vector<std::string>>& data) {
data.erase(std::remove_if(data.begin(), data.end(), [](const std::vector<std::string>& row) {
return row.empty() || std::any_of(row.begin(), row.end(), [](const std::string& value) { return value.empty(); });
}), data.end());
return data;
}
std::vector<std::vector<std::string>> formatData(std::vector<std::vector<std::string>>& data) {
// Example formatting: converting to lower case
for (auto& row : data) {
for (auto& value : row) {
std::transform(value.begin(), value.end(), value.begin(), ::tolower);
}
}
return data;
}
特征提取模块：
静态特征提取 动态特征提取
#include <openssl/md5.h>
#include <iomanip>
std::string hashFile(const std::string& filePath) {
std::ifstream file(filePath, std::ios::binary);
MD5_CTX md5Context;
MD5_Init(&md5Context);
char buffer[1024];
while (file.read(buffer, sizeof(buffer))) {
MD5_Update(&md5Context, buffer, file.gcount());
}
unsigned char result[MD5_DIGEST_LENGTH];
MD5_Final(result, &md5Context);
std::stringstream ss;
for (int i = 0; i < MD5_DIGEST_LENGTH; ++i) {
ss << std::hex << std::setw(2) << std::setfill('0') << (int)result[i];
}
return ss.str();
}
std::vector<std::string> staticFeatureExtraction(const std::vector<std::string>& filePaths) {
std::vector<std::string> hashes;
for (const auto& filePath : filePaths) {
hashes.push_back(hashFile(filePath));
}
return hashes;
}
// Placeholder for dynamic feature extraction
std::vector<float> dynamicFeatureExtraction(const std::vector<std::string>& filePaths) {
std::vector<float> features(filePaths.size(), 0.5f); // Placeholder values
return features;
}
模型训练模块：
模型选择 模型训练 模型评估
#include <opencv2/opencv.hpp>
#include <opencv2/ml/ml.hpp>
using namespace cv;
using namespace cv::ml;
Ptr<RTrees> trainModel(const Mat& features, const Mat& labels) {
Ptr<RTrees> model = RTrees::create();
model->setMaxDepth(10);
model->setMinSampleCount(10);
model->setRegressionAccuracy(0);
model->setUseSurrogates(false);
model->setMaxCategories(15);
model->setPriors(Mat());
model->setCalculateVarImportance(true);
model->setActiveVarCount(4);
model->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 100, 0.01));
model->train(features, ROW_SAMPLE, labels);
return model;
}
漏洞检测模块：
输入数据检测 漏洞分类 报告生成
std::vector<int> detectVulnerabilities(Ptr<RTrees> model, const Mat& features) {
std::vector<int> predictions;
for (int i = 0; i < features.rows; ++i) {
predictions.push_back((int)model->predict(features.row(i)));
}
return predictions;
}
std::vector<std::string> classifyVulnerability(const std::vector<int>& vulnerabilities) {
std::vector<std::string> classifications;
for (const auto& vuln : vulnerabilities) {
classifications.push_back(vuln == 1 ? "Critical" : "Non-Critical");
}
return classifications;
}
void generateReport(const std::vector<std::string>& filePaths, const std::vector<std::string>& classifications) {
std::ofstream report("vulnerability_report.csv");
report << "File Path,Vulnerability Classification\n";
for (size_t i = 0; i < filePaths.size(); ++i) {
report << filePaths[i] << "," << classifications[i] << "\n";
}
report.close();
std::cout << "Report generated: vulnerability_report.csv\n";
}
结果分析模块：
结果汇总 结果可视化 报告生成
void summarizeResults(const std::vector<std::string>& classifications) {
int criticalCount = std::count(classifications.begin(), classifications.end(), "Critical");
int nonCriticalCount = classifications.size() - criticalCount;
std::cout << "Critical Vulnerabilities: " << criticalCount << "\n";
std::cout << "Non-Critical Vulnerabilities: " << nonCriticalCount << "\n";
}
void visualizeResults(const std::vector<std::string>& classifications) {
int criticalCount = std::count(classifications.begin(), classifications.end(), "Critical");
int nonCriticalCount = classifications.size() - criticalCount;
// Simple text-based visualization
std::cout << "Critical: " << std::string(criticalCount, '*') << "\n";
std::cout << "Non-Critical: " << std::string(nonCriticalCount, '*') << "\n";
}
综合脚本
int main() {
// Load and preprocess data
auto data = loadData("input_files.csv");
data = cleanData(data);
data = formatData(data);
// Feature extraction
std::vector<std::string> filePaths;
for (const auto& row : data) {
filePaths.push_back(row[0]);
}
auto staticFeatures = staticFeatureExtraction(filePaths);
auto dynamicFeatures = dynamicFeatureExtraction(filePaths);
// Prepare data for model training
Mat features(staticFeatures.size(), 2, CV_32F);
for (size_t i = 0; i < staticFeatures.size(); ++i) {
features.at<float>(i, 0) = std::stof(staticFeatures[i]);
features.at<float>(i, 1) = dynamicFeatures[i];
}
Mat labels(staticFeatures.size(), 1, CV_32S);
for (size_t i = 0; i < data.size(); ++i) {
labels.at<int>(i, 0) = std::stoi(data[i][1]);
}
// Train model
auto model = trainModel(features, labels);
// Load new data for vulnerability detection
auto newData = loadData("new_files.csv");
newData = cleanData(newData);
newData = formatData(newData);
std::vector<std::string> newFilePaths;
for (const auto& row : newData) {
newFilePaths.push_back(row[0]);
}
auto newStaticFeatures = staticFeatureExtraction(newFilePaths);
auto newDynamicFeatures = dynamicFeatureExtraction(newFilePaths);
// Prepare new data for detection
Mat newFeatures(newStaticFeatures.size(), 2, CV_32F);
for (size_t i = 0; i < newStaticFeatures.size(); ++i) {
newFeatures.at<float>(i, 0) = std::stof(newStaticFeatures[i]);
newFeatures.at<float>(i, 1) = newDynamicFeatures[i];
}
// Detect vulnerabilities
auto vulnerabilities = detectVulnerabilities(model, newFeatures);
auto classifications = classifyVulnerability(vulnerabilities);
generateReport(newFilePaths, classifications);
// Analyze results
summarizeResults(classifications);
visualizeResults(classifications);
return 0;
}
这个C++代码示例假设你有两个数据文件：input_files.csv（用于训练模型的输入文件）和 new_files.csv（用于检测的新文件）。代码会加载数据、预处理、提取特征、训练模型、检测漏洞并生成报告。你可以根据需要调整这些步骤中的细节
