src/ai_model.py

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)