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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)
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