Fortifying AI Against Cyber Threats Advancing Resilient Systems to Combat Adversarial Attacks

Abstract

The emerging complexity of the threats which the safer world faces as of now, let alone the adversary attacks, presents great troubles to the orthodox systems of cybersecurity. This research focuses on the use of AI to design and develop robust systems that prevent such shifting threats. As a result, the proposed adaptive defense mechanisms utilize RL in this investigation to contend with real-time emergent attack patterns. At the same time, Ensemble Methods are applied to improve the anomaly detection non-sensitive approaches by using several machine learning models in order to minimize the false positives and maximize the true positives. The utility of the proposed framework is assessed by using benchmarking data sets such as NSL-KDD and CICIDS 2017 to represent various adversarial settings. The results shown in the study show that RL is able to manage dynamic threats successfully with high detection ratio and low response time. Ensemble Methods supplement this by strengthening the reliability of detection and shortening the error margin. The study reveals that AI has implications for optimizing the possibilities of cybersecurity systems, as well as for the defense systems themselves, as the implementation of flexible and fundamentally suitable approaches is already possible at the present stage.