Real-Time Cyber Threats and Unauthorized Access Detection Using Embedded AI

As cyber-physical systems (CPS) and IoT ecosystems grow, protection of these interlinked environments against cyber-attacks and unauthorized access is now a high priority imperative. Conventional centralized security paradigms are becoming less effective with high latency and poor scalability, necessitating real-time, embedded solutions. This work suggests an AI driven embedded system that can identify cyber-attacks and unauthorized access in real time utilizing deep learning models and federated learning. Lightweight neural networks are embedded at the edge to carry out continuous anomaly detection and classification, while dynamic model updating is achieved through cloud driven federated learning. A cyber twin is utilized to perform real-time simulation of attack scenarios and forecast newly emerging vulnerabilities. The AI subsystem embedded identifies recognized and unrecognized threats by utilizing the hybrid models in fusion with CNNs, LSTMs, and auto-encoders in a manner of achieving high accuracy of detection along with negligible delay. Performance validation tests using common benchmarks like CICIDS2017 and NSL-KDD reveal superior quality of performance resulting in higher accuracy of detection, fewer false alarms, and decreased response times for traditional threat detecting mechanisms. The envisioned system delivers a scalable and responsive solution that augments the security of IoT and CPS landscapes against emerging cyber-attacks.