Autonomous AI Controlled Cyber Attack Prediction System Design

Concurrently sophisticated online dangers have proven that conventional rule-based security approaches fail to identify current attacks effectively. A self-governing AI system with hybrid deep learning models for immediate security threat recognition functions as a solution to solve this issue. The designed system merges LSTM technology with Transformer-based system features to detect network traffic patterns for precise attack prediction. Evaluation conducted on the CICIDS2017 and NSL-KDD datasets demonstrated a 97.5% accuracy rate lower false positive rates by 30% and superior performance than traditional models with 5-7% improvement. The system proved its capability to boost security defenses due to its ability to spot zero-day assaults and develop threat defense capabilities. The implemented model delivers efficient and scalable proactive security capabilities which position it as an effective solution for delivering real-time cybersecurity applications.