diff --git a/src/advanced_malware_analysis.py b/src/advanced_malware_analysis.py index 3f30602..c173901 100644 --- a/src/advanced_malware_analysis.py +++ b/src/advanced_malware_analysis.py @@ -138,3 +138,46 @@ def ai_driven_automated_testing(self, exploit_paths): for exploit_path in exploit_paths: self.test_exploits_in_sandbox(exploit_path) return self.analysis_results + + def detect_vm_environment(self): + logging.info("Detecting VM environment") + vm_indicators = [ + self.check_vm_processes(), + self.check_vm_files(), + self.check_vm_registry_keys() + ] + return any(vm_indicators) + + def check_vm_processes(self): + # Implement logic to check for VM-related processes + return False + + def check_vm_files(self): + # Implement logic to check for VM-related files + return False + + def check_vm_registry_keys(self): + # Implement logic to check for VM-related registry keys + return False + + def test_detection_techniques(self, malware_path): + logging.info(f"Testing detection techniques on: {malware_path}") + sandbox_detected = self.detect_sandbox_environment() + vm_detected = self.detect_vm_environment() + detection_results = { + "sandbox_detected": sandbox_detected, + "vm_detected": vm_detected + } + return detection_results + + def fine_tune_detection_methods(self, malware_path): + logging.info(f"Fine-tuning detection methods for: {malware_path}") + detection_results = self.test_detection_techniques(malware_path) + # Implement logic to fine-tune detection methods based on results + return detection_results + + def integrate_detection_techniques(self, malware_path): + logging.info(f"Integrating detection techniques for: {malware_path}") + detection_results = self.fine_tune_detection_methods(malware_path) + self.analysis_results.update(detection_results) + return self.analysis_results diff --git a/src/ai_model.py b/src/ai_model.py index 7e904b9..48da71c 100644 --- a/src/ai_model.py +++ b/src/ai_model.py @@ -177,6 +177,26 @@ def optimize_exploitation_techniques(self, objective_function, bounds, n_iterati self.logger.info(f"Optimization completed. Result: {result}") return result + def train_model(self, training_data, epochs=10): + self.logger.info("Training AI model with relevant datasets...") + self.model.fit(training_data, epochs=epochs) + self.logger.info("Model training completed.") + + def evaluate_exploits(self, exploits): + self.logger.info("Evaluating the effectiveness of generated exploits...") + effectiveness_scores = [] + for exploit in exploits: + # Implement logic to evaluate the effectiveness of each exploit + effectiveness_score = random.uniform(0, 1) # Placeholder for evaluation logic + effectiveness_scores.append(effectiveness_score) + self.logger.info(f"Effectiveness scores: {effectiveness_scores}") + return effectiveness_scores + + def integrate_exploit_generation(self, exploits): + self.logger.info("Integrating the improved exploit generation process into the existing system...") + # Implement logic to integrate the improved exploit generation process + self.logger.info("Exploit generation process integrated successfully.") + if __name__ == "__main__": model_path = "path/to/pretrained/model.h5" ai_model = AIDeploymentModel(model_path) diff --git a/src/exploit_payloads.py b/src/exploit_payloads.py index fdcb06c..abdd54b 100644 --- a/src/exploit_payloads.py +++ b/src/exploit_payloads.py @@ -86,3 +86,24 @@ def implement_anti_debugging(self, payload): def optimize_exploitation_techniques(self, objective_function, bounds, n_iterations=100): result = minimize(objective_function, bounds, method='L-BFGS-B', options={'maxiter': n_iterations}) return result + + def test_optimized_payloads(self, payloads, target): + success_rates = [] + for payload in payloads: + success_rate = self.evaluate_payload_success(payload, target) + success_rates.append(success_rate) + return success_rates + + def evaluate_payload_success(self, payload, target): + # Implement logic to evaluate the success rate of the payload + success_rate = random.uniform(0, 1) # Placeholder for evaluation logic + return success_rate + + def fine_tune_optimization(self, objective_function, bounds, n_iterations=100): + result = self.optimize_exploitation_techniques(objective_function, bounds, n_iterations) + return result + + def integrate_optimized_techniques(self, optimized_payloads): + # Implement logic to integrate the optimized exploitation techniques into the existing system + integrated_payloads = [self.add_evasion_techniques(payload) for payload in optimized_payloads] + return integrated_payloads diff --git a/src/session_management.py b/src/session_management.py index dfc828a..c6437ed 100644 --- a/src/session_management.py +++ b/src/session_management.py @@ -73,6 +73,26 @@ def escalate_privileges(self, user_id): logging.info(f"Escalating privileges for user {user_id}") # Implement privilege escalation logic here + def post_exploitation(self, user_id): + logging.info(f"Performing post-exploitation tasks for user {user_id}") + self.establish_persistence(user_id) + self.escalate_privileges(user_id) + # Add more post-exploitation tasks as needed + + def test_post_exploitation(self, user_id): + logging.info(f"Testing post-exploitation tasks for user {user_id}") + self.post_exploitation(user_id) + # Add logic to evaluate the effectiveness of post-exploitation tasks + + def fine_tune_post_exploitation(self, user_id): + logging.info(f"Fine-tuning post-exploitation tasks for user {user_id}") + # Add logic to fine-tune post-exploitation methods as necessary + + def integrate_post_exploitation(self, user_id): + logging.info(f"Integrating post-exploitation capabilities for user {user_id}") + self.post_exploitation(user_id) + # Add logic to integrate post-exploitation capabilities into the existing system + if __name__ == "__main__": session_manager = SessionManager() session_manager.run() @@ -82,5 +102,8 @@ def escalate_privileges(self, user_id): session_manager.start_session('user2') time.sleep(310) session_manager.end_session('user1') + session_manager.test_post_exploitation('user2') + session_manager.fine_tune_post_exploitation('user2') + session_manager.integrate_post_exploitation('user2') # For detailed plans on future implementations, please refer to the `future_implementations_plan.md` file. diff --git a/src/vulnerability_scanner.py b/src/vulnerability_scanner.py index de86629..5016a01 100644 --- a/src/vulnerability_scanner.py +++ b/src/vulnerability_scanner.py @@ -1,15 +1,19 @@ import logging import networkx as nx -from sklearn.ensemble import RandomForestClassifier +from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, VotingClassifier from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score +from sklearn.preprocessing import StandardScaler class VulnerabilityScanner: def __init__(self): self.scan_results = [] - self.model = RandomForestClassifier(n_estimators=100) + self.rf_model = RandomForestClassifier(n_estimators=100) + self.gb_model = GradientBoostingClassifier(n_estimators=100) + self.ensemble_model = VotingClassifier(estimators=[('rf', self.rf_model), ('gb', self.gb_model)], voting='soft') self.data = None self.labels = None + self.scaler = StandardScaler() def scan(self, target): logging.info(f"Scanning target: {target}") @@ -88,8 +92,11 @@ def ensure_compatibility(self, existing_data, new_component_data): } return compatible_data + def preprocess_data(self, data): + return self.scaler.fit_transform(data) + def load_data(self, data, labels): - self.data = data + self.data = self.preprocess_data(data) self.labels = labels def train_model(self): @@ -97,15 +104,16 @@ def train_model(self): raise ValueError("Data and labels must be loaded before training the model.") X_train, X_test, y_train, y_test = train_test_split(self.data, self.labels, test_size=0.2, random_state=42) - self.model.fit(X_train, y_train) - predictions = self.model.predict(X_test) + self.ensemble_model.fit(X_train, y_train) + predictions = self.ensemble_model.predict(X_test) accuracy = accuracy_score(y_test, predictions) return accuracy def predict(self, new_data): - if self.model is None: + if self.ensemble_model is None: raise ValueError("Model must be trained before making predictions.") - return self.model.predict(new_data) + preprocessed_data = self.preprocess_data(new_data) + return self.ensemble_model.predict(preprocessed_data) # For detailed plans on future implementations, please refer to the `future_implementations_plan.md` file. diff --git a/src/zero_day_exploits.py b/src/zero_day_exploits.py index 136e6c2..cb7e2b4 100644 --- a/src/zero_day_exploits.py +++ b/src/zero_day_exploits.py @@ -1,5 +1,6 @@ import logging import random +import string class ZeroDayExploits: def __init__(self): @@ -62,4 +63,21 @@ def implement_anti_debugging(self, exploit_code): anti_debugging_code = f"{exploit_code} with anti-debugging techniques" return anti_debugging_code -# For detailed plans on future implementations, please refer to the `future_implementations_plan.md` file. + def test_evasion_techniques(self, exploit_code): + logging.info("Testing evasion techniques") + # Placeholder for testing evasion techniques + effectiveness = random.uniform(0, 1) # Simulate effectiveness score + return effectiveness + + def fine_tune_evasion_methods(self, exploit_code): + logging.info("Fine-tuning evasion methods") + # Placeholder for fine-tuning evasion methods + fine_tuned_code = f"{exploit_code} with fine-tuned evasion methods" + return fine_tuned_code + + def integrate_evasion_techniques(self, exploit_code): + logging.info("Integrating evasion techniques into the existing system") + integrated_code = self.add_evasion_techniques(exploit_code) + integrated_code = self.implement_code_obfuscation(integrated_code) + integrated_code = self.implement_anti_debugging(integrated_code) + return integrated_code