import numpy as np import tensorflow as tf from tensorflow.keras.models import load_model import logging import random from scipy.optimize import minimize class AIDeploymentModel: def __init__(self, model_path): self.model = load_model(model_path) self.setup_logging() self.supported_models = ["model1.h5", "model2.h5", "model3.h5"] def setup_logging(self): logging.basicConfig(filename='logs/ai_model.log', level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') self.logger = logging.getLogger(__name__) self.logger.addHandler(logging.StreamHandler()) self.logger.info("Logging setup complete.") self.add_notification_system() def preprocess_input(self, input_data): # Implement preprocessing logic here return np.array(input_data) def predict(self, input_data): if not input_data: self.logger.error("Input data is empty.") return None preprocessed_data = self.preprocess_input(input_data) predictions = self.model.predict(preprocessed_data) self.logger.info(f"Predictions: {predictions}") # Add AI-driven analysis and detection for Pegasus Spyware's Zero-Click iPhone Exploit ForcedEntry if "pegasus_forcedentry" in input_data: self.logger.info("Detected Pegasus Spyware's Zero-Click iPhone Exploit ForcedEntry") predictions = self.analyze_pegasus_forcedentry(input_data) return predictions def analyze_pegasus_forcedentry(self, input_data): # Implement AI-driven analysis and detection logic for Pegasus Spyware's Zero-Click iPhone Exploit ForcedEntry self.logger.info("Analyzing Pegasus Spyware's Zero-Click iPhone Exploit ForcedEntry") # Placeholder for analysis logic analysis_results = {"detection": "Pegasus ForcedEntry detected", "severity": "high"} return analysis_results def deploy_exploit(self, target_info): predictions = self.predict(target_info) # Implement logic to deploy exploits based on predictions self.logger.info(f"Deploying exploit with predictions: {predictions}") # Update deploy_exploit method to handle Pegasus Spyware's Zero-Click iPhone Exploit ForcedEntry if "pegasus_forcedentry" in target_info: self.logger.info("Deploying Pegasus Spyware's Zero-Click iPhone Exploit ForcedEntry") self.deploy_pegasus_forcedentry(target_info) self.integrate_chatbot_assistant() return predictions def deploy_pegasus_forcedentry(self, target_info): # Implement logic to deploy Pegasus Spyware's Zero-Click iPhone Exploit ForcedEntry self.logger.info("Deploying Pegasus Spyware's Zero-Click iPhone Exploit ForcedEntry") # Placeholder for deployment logic deployment_results = {"status": "success", "details": "Pegasus ForcedEntry exploit deployed"} return deployment_results def scan_targets(self): # Implement logic to scan targets self.logger.info("Scanning targets...") # Placeholder for scanning logic targets = ["target1", "target2", "target3"] self.logger.info(f"Targets found: {targets}") return targets def modify_exploits(self, target_info): # Implement logic to modify exploits based on target information self.logger.info(f"Modifying exploits for target: {target_info}") # Placeholder for modification logic modified_exploits = ["exploit1", "exploit2", "exploit3"] self.logger.info(f"Modified exploits: {modified_exploits}") return modified_exploits def test_predictions(self, labeled_data): # Implement logic to test predictions for accuracy self.logger.info("Testing predictions for accuracy...") # Placeholder for testing logic accuracy = 0.95 self.logger.info(f"Prediction accuracy: {accuracy}") self.implement_reporting_feature() return accuracy def add_model(self, model_path): if model_path not in self.supported_models: self.supported_models.append(model_path) self.logger.info(f"Model {model_path} added to supported models.") else: self.logger.info(f"Model {model_path} is already supported.") def load_model(self, model_path): if model_path in self.supported_models: self.model = load_model(model_path) self.logger.info(f"Model {model_path} loaded successfully.") else: self.logger.error(f"Model {model_path} is not supported.") def ai_driven_vulnerability_scanning(self, target_systems): self.logger.info("Starting AI-driven vulnerability scanning...") vulnerabilities = [] for system in target_systems: # Implement AI-driven vulnerability scanning logic here self.logger.info(f"Scanning system: {system}") # Placeholder for scanning logic system_vulnerabilities = ["vuln1", "vuln2", "vuln3"] vulnerabilities.append({system: system_vulnerabilities}) self.logger.info(f"Vulnerability scanning completed. Results: {vulnerabilities}") self.integrate_vulnerability_scanner() return vulnerabilities def predict_success_rate(self, exploits): # Implement logic to predict the success rate of different exploits self.logger.info("Predicting success rate of exploits...") # Placeholder for prediction logic success_rates = [0.8, 0.9, 0.7] self.logger.info(f"Success rates: {success_rates}") return success_rates def continuously_train_model(self, new_data): # Implement logic to continuously train the AI model with new data self.logger.info("Continuously training AI model with new data...") # Placeholder for training logic self.model.fit(new_data, epochs=10) self.logger.info("Model training completed.") def add_notification_system(self): # Add a notification system to alert users of important events or updates within the app pass def integrate_chatbot_assistant(self): # Integrate a chatbot to assist users with common tasks and provide guidance pass def integrate_vulnerability_scanner(self): # Integrate a vulnerability scanner to identify potential security issues in target systems pass def implement_reporting_feature(self): # Implement a reporting feature to generate detailed reports on exploit activities and results pass def train_hak5_model(self, training_data): self.logger.info("Training AI model for generating Hak5 Ducky Script payloads...") # Implement logic to train the AI model with Hak5 Ducky Script payloads self.model.fit(training_data, epochs=10) self.logger.info("Hak5 model training completed.") def ai_driven_vulnerability_scanning(self, target_info): self.logger.info("Starting AI-driven vulnerability scanning...") vulnerabilities = [] for target in target_info: # Implement AI-driven vulnerability scanning logic here self.logger.info(f"Scanning target: {target}") # Placeholder for scanning logic target_vulnerabilities = ["vuln1", "vuln2", "vuln3"] vulnerabilities.append({target: target_vulnerabilities}) self.logger.info(f"Vulnerability scanning completed. Results: {vulnerabilities}") return vulnerabilities def reinforcement_learning_exploit_generation(self, environment, policy, episodes=1000): self.logger.info("Starting reinforcement learning for exploit generation...") for episode in range(episodes): state = environment.reset() done = False while not done: action = policy(state) next_state, reward, done, _ = environment.step(action) policy.update(state, action, reward, next_state) state = next_state self.logger.info("Reinforcement learning for exploit generation completed.") return policy def bayesian_optimization_exploitation(self, objective_function, bounds, n_iterations=100): self.logger.info("Starting Bayesian optimization for exploitation process...") result = minimize(objective_function, bounds, method='L-BFGS-B', options={'maxiter': n_iterations}) self.logger.info(f"Bayesian optimization completed. Result: {result}") return result def generate_exploits_with_reinforcement_learning(self, environment, policy, episodes=1000): self.logger.info("Starting reinforcement learning for exploit generation...") for episode in range(episodes): state = environment.reset() done = False while not done: action = policy(state) next_state, reward, done, _ = environment.step(action) policy.update(state, action, reward, next_state) state = next_state self.logger.info("Reinforcement learning for exploit generation completed.") return policy def optimize_exploitation_techniques(self, objective_function, bounds, n_iterations=100): self.logger.info("Starting optimization of exploitation techniques...") result = minimize(objective_function, bounds, method='L-BFGS-B', options={'maxiter': n_iterations}) 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) target_info = [/* target information */] predictions = ai_model.deploy_exploit(target_info) print(predictions)