Artificial Intelligence Assisted Cyber-Physical Systems with Intelligent Cyber Security Using Deep Learning

Cyber-physical systems, automated anomaly recognition, and threat prevention and response are just a few of the high-tech systems that people depend on today for essential functions that directly affect their safety and, ultimately, their level of life. However, recent tragedies have proved that even highly individualized human anomaly monitoring in complex systems is inadequate, highlighting the need for automated detection and intelligent replies as a proposed security strategy. AI can detect security breaches and other events with the help of machine learning and deep learning, which enable it to organize network patterns and recognize anomalies. This paper introduces the deep learning-assisted cyber-physical systems (DL-CPS) framework. Smartly suggesting an unusual detection mechanism for complex systems reliant on surveyed information storage or numerical corrections analysis, time series analyses of a multivariate time series section, and uncontrolled depth studying to differentiate healthy and unhealthy behaviour for various components. The results of the tests reveal that the suggested model is much better than the baseline and accurately describes the nonlinear temporal correlation and transient patterns.