Intelligent Attack Prediction Using Markov Decision Processes
Our advanced system leverages Markov Decision Processes (MDP) to predict and prevent cyber-attacks in real-time. By analyzing network traffic patterns and suspicious indicators, the system computes optimal defensive actions using the Value Iteration algorithm. The MDP framework evaluates all possible future states and recommends the best course of action to maximize security while minimizing operational costs.
Uses Value Iteration algorithm to compute optimal defense policies across 6 security states
Instantly analyzes network traffic and suspicious indicators to determine current threat level
Comprehensive visualizations showing state probabilities and action Q-values for informed decisions
Balances security effectiveness with operational costs to recommend economically optimal actions
Random Forest classifiers trained on NSL-KDD dataset with 75% accuracy for attack type identification
Upload and analyze large datasets with comprehensive statistics and visualizations