Federated Adversarial-AI for Zero-Trust Explainable Cybersecurity

Abstract

The current trends in the cyber threat landscape of distributed systems have required a paradigm shift to decentralized and thrustless security. The research suggests a new architecture, Federated Adversarial-AI for Zero-Trust Explainable Cybersecurity (FAZTEC), combining federated learning and adversarial artificial intelligence to help make the cybersecurity systems more resilient, and explainable. The proposed framework, with the help of federated learning, would allow interconnected threat detection on edge devices, which does not require sharing raw data since it would keep privacy and meet the criteria of regulatory requirements. The same happens through the use of adversarial AI in order to simulate advanced attack scenarios and thus strengthen the defines mechanisms of the threats that are evolving. Auditioning explainable AI (XAI) modules also increases transparency in the system, where the security analyst can understand and verify the detection results in real-time. The zero-trust architecture also verifies a device, user, and data flow continuously, which discards the implicit assumptions about trustworthiness. A wide range of experiments performed in various network environments proves the effectiveness, validity, and interpretability of FAZTEC, which represents a serious breakthrough in proactive cybersecurity protection. The work is useful to the future of security infrastructure, which is smart, decentralized and explainable, and applicable to critical applications in finance, healthcare, and government.