Implement advanced code logic for future implementations

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

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)

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

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.

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}")
@@ -88,24 +92,28 @@ 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):
         if self.data is None or self.labels is None:
             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.

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