rwarmor_components.cpp

#include "rwarmor.h"
#include <algorithm>
#include <random>
#include <cmath>

// FileMonitor implementation

FileMonitor::FileMonitor() 
    : is_monitoring_(false) {
}

FileMonitor::~FileMonitor() {
    stopMonitoring();
}

bool FileMonitor::initialize() {
    std::cout << "Initializing file monitor..." << std::endl;
    return true;
}

bool FileMonitor::startMonitoring() {
    if (is_monitoring_) {
        std::cout << "File monitoring is already active" << std::endl;
        return true;
    }
    
    // Start monitoring thread
    is_monitoring_ = true;
    monitor_thread_ = std::thread(&FileMonitor::monitorFileSystem, this);
    
    std::cout << "File monitoring started" << std::endl;
    return true;
}

void FileMonitor::stopMonitoring() {
    if (!is_monitoring_) {
        return;
    }
    
    // Stop monitoring
    is_monitoring_ = false;
    
    // Wait for monitoring thread to finish
    if (monitor_thread_.joinable()) {
        monitor_thread_.join();
    }
    
    std::cout << "File monitoring stopped" << std::endl;
}

std::vector<std::string> FileMonitor::getSuspiciousOperations() {
    std::lock_guard<std::mutex> lock(mutex_);
    
    // Get copy of suspicious operations
    std::vector<std::string> operations = suspicious_operations_;
    
    // Clear the internal list
    suspicious_operations_.clear();
    
    return operations;
}

void FileMonitor::monitorFileSystem() {
    while (is_monitoring_) {
        // This is a placeholder for real file system monitoring
        // In a real implementation, this would use platform-specific APIs
        // to monitor file system changes (e.g., ReadDirectoryChangesW on Windows)
        
        // For demo purposes, just sleep
        std::this_thread::sleep_for(std::chrono::milliseconds(500));
    }
}

bool FileMonitor::isSuspiciousOperation(const std::string& operation) {
    // Check if this is a suspicious file operation
    // For example, multiple write operations to files with common extensions
    // or deleting multiple files in succession
    
    // For demo purposes, just check if operation contains certain keywords
    bool is_suspicious = false;
    
    if (operation.find("encrypt") != std::string::npos || 
        operation.find("delete") != std::string::npos ||
        operation.find(".doc") != std::string::npos ||
        operation.find(".xls") != std::string::npos ||
        operation.find(".pdf") != std::string::npos ||
        operation.find(".jpg") != std::string::npos) {
        is_suspicious = true;
    }
    
    return is_suspicious;
}

// MLModel implementation

MLModel::MLModel() 
    : model_impl_(nullptr), 
      num_features_(0), 
      is_trained_(false) {
}

MLModel::~MLModel() {
    // Cleanup model implementation if necessary
    // This is a placeholder since we don't have a real ML model
}

bool MLModel::initialize() {
    std::cout << "Initializing ML model..." << std::endl;
    
    // In a real implementation, this would initialize the ML model
    // For demo purposes, just set some default values
    num_features_ = 20;  // Number of features expected by the model
    
    return true;
}

bool MLModel::loadModel(const std::string& model_file) {
    std::cout << "Loading ML model from: " << model_file << std::endl;
    
    // In a real implementation, this would load a trained model
    // For demo purposes, just set the trained flag
    is_trained_ = true;
    
    return true;
}

bool MLModel::saveModel(const std::string& model_file) {
    if (!is_trained_) {
        std::cerr << "Cannot save untrained model" << std::endl;
        return false;
    }
    
    std::cout << "Saving ML model to: " << model_file << std::endl;
    
    // In a real implementation, this would save the trained model
    return true;
}

bool MLModel::train(const std::vector<std::vector<float>>& features, 
                   const std::vector<int>& labels) {
    if (features.empty() || labels.empty() || features.size() != labels.size()) {
        std::cerr << "Invalid training data" << std::endl;
        return false;
    }
    
    std::cout << "Training ML model with " << features.size() << " samples..." << std::endl;
    
    // In a real implementation, this would train the ML model using features and labels
    // For demo purposes, just set the trained flag
    is_trained_ = true;
    
    // If there are any features, update the expected number of features
    if (!features.empty()) {
        num_features_ = features[0].size();
    }
    
    return true;
}

float MLModel::predict(const std::vector<float>& features) {
    if (!is_trained_) {
        std::cerr << "Cannot predict with untrained model" << std::endl;
        return 0.0f;
    }
    
    if (features.size() != num_features_) {
        std::cerr << "Feature vector size mismatch (expected " << num_features_ 
                  << ", got " << features.size() << ")" << std::endl;
        return 0.0f;
    }
    
    // In a real implementation, this would use the ML model to predict
    // For demo purposes, just return a random value or simple heuristic
    
    // Here we use a simple heuristic: average of all feature values
    float sum = 0.0f;
    for (const auto& feature : features) {
        sum += feature;
    }
    
    return sum / features.size();
}

// APIHook implementation

APIHook::APIHook() {
}

APIHook::~APIHook() {
    // Unhook all hooked APIs
    for (const auto& entry : original_functions_) {
        // In a real implementation, this would unhook the API
    }
}

bool APIHook::initialize() {
    std::cout << "Initializing API hooks..." << std::endl;
    return true;
}

bool APIHook::hookAPI(const std::string& module_name, const std::string& function_name) {
    std::string full_name = module_name + ":" + function_name;
    
    // Check if already hooked
    if (original_functions_.find(full_name) != original_functions_.end()) {
        return true;  // Already hooked
    }
    
    std::cout << "Hooking API: " << full_name << std::endl;
    
    // In a real implementation, this would hook the API function
    // For demo purposes, just add an entry to the map
    // In Windows, you'd use techniques like IAT hooking, detours, etc.
    original_functions_[full_name] = nullptr;
    
    return true;
}

bool APIHook::unhookAPI(const std::string& module_name, const std::string& function_name) {
    std::string full_name = module_name + ":" + function_name;
    
    // Check if not hooked
    if (original_functions_.find(full_name) == original_functions_.end()) {
        return true;  // Not hooked
    }
    
    std::cout << "Unhooking API: " << full_name << std::endl;
    
    // In a real implementation, this would unhook the API function
    // For demo purposes, just remove the entry from the map
    original_functions_.erase(full_name);
    
    return true;
}

std::vector<std::string> APIHook::getAPICalls(uint32_t process_id) {
    std::lock_guard<std::mutex> lock(mutex_);
    
    // Return empty vector if process not found
    if (api_calls_.find(process_id) == api_calls_.end()) {
        return std::vector<std::string>();
    }
    
    return api_calls_[process_id];
}

void APIHook::clearAPICalls(uint32_t process_id) {
    std::lock_guard<std::mutex> lock(mutex_);
    
    // Clear API calls for the process
    api_calls_.erase(process_id);
}

void APIHook::apiHookCallback(uint32_t process_id, const std::string& api_name, 
                             const std::string& params) {
    // This is a static method, so we need an instance to call non-static methods
    // In a real implementation, we'd use a singleton or global instance
    // For demo purposes, just use a placeholder
    
    // Log the API call
    std::cout << "Process " << process_id << " called " << api_name 
              << " with params: " << params << std::endl;
    
    // In a real implementation, we'd add this to the api_calls_ map
}

// FeatureExtractor implementation

FeatureExtractor::FeatureExtractor() {
}

FeatureExtractor::~FeatureExtractor() {
}

bool FeatureExtractor::initialize() {
    std::cout << "Initializing feature extractor..." << std::endl;
    
    // Initialize feature dictionary
    feature_names_ = {
        "import_count",
        "crypto_import_count",
        "network_import_count",
        "file_import_count",
        "registry_import_count",
        "header_size",
        "section_count",
        "executable_sections",
        "writable_sections",
        "suspicious_section_names",
        "entry_point_section",
        "string_count",
        "suspicious_string_count",
        "url_count",
        "registry_string_count",
        "file_string_count",
        "encryption_string_count",
        "entropy_exe",
        "entropy_data",
        "entropy_overall"
    };
    
    return true;
}

std::vector<float> FeatureExtractor::extractFeatures(const std::string& file_path) {
    std::vector<float> features;
    
    // Extract various feature types
    auto import_features = extractImportFeatures(file_path);
    auto header_features = extractHeaderFeatures(file_path);
    auto string_features = extractStringFeatures(file_path);
    auto entropy_features = extractEntropyFeatures(file_path);
    
    // Combine all features
    features.insert(features.end(), import_features.begin(), import_features.end());
    features.insert(features.end(), header_features.begin(), header_features.end());
    features.insert(features.end(), string_features.begin(), string_features.end());
    features.insert(features.end(), entropy_features.begin(), entropy_features.end());
    
    return features;
}

std::vector<float> FeatureExtractor::extractImportFeatures(const std::string& file_path) {
    // This is a placeholder for real import feature extraction
    // In a real implementation, this would parse the PE file and extract import table
    
    // For demo purposes, just return some random values
    std::random_device rd;
    std::mt19937 gen(rd());
    std::uniform_real_distribution<float> dis(0.0f, 1.0f);
    
    // Return 5 import features
    return {
        dis(gen),  // import_count
        dis(gen),  // crypto_import_count
        dis(gen),  // network_import_count
        dis(gen),  // file_import_count
        dis(gen)   // registry_import_count
    };
}

std::vector<float> FeatureExtractor::extractHeaderFeatures(const std::string& file_path) {
    // This is a placeholder for real header feature extraction
    // In a real implementation, this would parse the PE file and extract header info
    
    // For demo purposes, just return some random values
    std::random_device rd;
    std::mt19937 gen(rd());
    std::uniform_real_distribution<float> dis(0.0f, 1.0f);
    
    // Return 5 header features
    return {
        dis(gen),  // header_size
        dis(gen),  // section_count
        dis(gen),  // executable_sections
        dis(gen),  // writable_sections
        dis(gen)   // entry_point_section
    };
}

std::vector<float> FeatureExtractor::extractStringFeatures(const std::string& file_path) {
    // This is a placeholder for real string feature extraction
    // In a real implementation, this would extract strings from the file
    // and analyze them for suspicious patterns
    
    // For demo purposes, just return some random values
    std::random_device rd;
    std::mt19937 gen(rd());
    std::uniform_real_distribution<float> dis(0.0f, 1.0f);
    
    // Return 7 string features
    return {
        dis(gen),  // string_count
        dis(gen),  // suspicious_string_count
        dis(gen),  // url_count
        dis(gen),  // registry_string_count
        dis(gen),  // file_string_count
        dis(gen),  // encryption_string_count
        dis(gen)   // dll_string_count
    };
}

std::vector<float> FeatureExtractor::extractEntropyFeatures(const std::string& file_path) {
    // This is a placeholder for real entropy feature extraction
    // In a real implementation, this would calculate entropy of various sections
    
    // For demo purposes, just return some random values
    std::random_device rd;
    std::mt19937 gen(rd());
    std::uniform_real_distribution<float> dis(0.0f, 8.0f);  // Entropy is between 0 and 8
    
    // Return 3 entropy features
    return {
        dis(gen),  // entropy_exe
        dis(gen),  // entropy_data
        dis(gen)   // entropy_overall
    };
}

float FeatureExtractor::calculateEntropy(const std::vector<uint8_t>& data) {
    if (data.empty()) {
        return 0.0f;
    }
    
    // Count frequency of each byte value
    std::array<int, 256> frequency = {};
    for (uint8_t byte : data) {
        frequency[byte]++;
    }
    
    // Calculate entropy
    float entropy = 0.0f;
    for (int count : frequency) {
        if (count > 0) {
            float p = static_cast<float>(count) / data.size();
            entropy -= p * log2f(p);
        }
    }
    
    return entropy;
}

std::vector<std::vector<uint8_t>> FeatureExtractor::readPESections(const std::string& file_path) {
    // This is a placeholder for real PE section reading
    // In a real implementation, this would parse the PE file and extract sections
    
    // For demo purposes, just return empty vector
    return {};
}

std::vector<std::string> FeatureExtractor::readPEImports(const std::string& file_path) {
    // This is a placeholder for real PE import reading
    // In a real implementation, this would parse the PE file and extract imports
    
    // For demo purposes, just return empty vector
    return {};
}

std::vector<std::string> FeatureExtractor::extractStrings(const std::string& file_path) {
    // This is a placeholder for real string extraction
    // In a real implementation, this would extract strings from the file
    
    // For demo purposes, just return empty vector
    return {};
}