Update project documentation and enhance malware detection engine

- Completely rewrite README.md with comprehensive project overview and technical details
- Add detailed explanation of antivirus engine architecture and detection strategies
- Implement multi-stage malware detection with machine learning, sandbox, and PE structure analysis
- Update project configuration and add new source files for enhanced detection capabilities
- Integrate XGBoost machine learning model with C++ export functionality
- Improve sandbox environment with advanced module and LDR data table handling
- Remove legacy Python prediction and training scripts in favor of C++ implementation

ai_anti_malware/ml.h

@@ -66,6 +66,9 @@ inline BYTE* RvaToPtr(DWORD rva, BYTE* peBuffer) {
     return nullptr;
 }
 
+// 在头文件中声明score函数（从外部导入）
+extern double score(double* input);
+
 class MachineLearning {
    public:
     MachineLearning();
@@ -86,6 +89,10 @@ class MachineLearning {
     // 读取文件到内存
     std::vector<uint8_t> ReadFileToBuffer(const std::string& filePath);
 
+    // 新增方法：使用XGBoost模型预测文件是否为恶意软件
+    double PredictMalware(const uint8_t* buffer, size_t bufferSize);
+    double PredictMalwareFromFile(const std::string& filePath);
+
    private:
     // 特征提取辅助函数
     std::vector<double> EncodeProperties(