Adversarial AI-Powered Intrusion Detection: A Self-Evolving Framework Using Federated Learning

The convergence of artificial intelligence (AI), data analytics, and cybersecurity is creating both unprecedented defensive capabilities and novel attack surfaces.This paper outlines a structured research agenda spanning seven innovative directions at this intersection: (1) adversarial, federated intrusion detection frameworks that preserve privacy while resisting data poisoning; (2) explainable AI (XAI) models for insider threat detection that bridge the trust gap in Security Operations Centers ( SOCs); (3) deep reinforcement learning-based analysis of quantum resistant cryptographic implementations; (4) generative adversarial network (GAN)-driven synthetic attack generation for zero day exploit prediction; (5) privacy preserving anomaly detection in healthcare IoT using homomorphic encryption; (6) graph neural network (GNN) approaches for high confidence Advanced Persistent Threat (APT) campaign attribution; and ( 7) transformer based continuous authentication using behavioral biometrics.For each direction, we identify core concepts, key technical innovations, and concrete research questions, emphasizing trade offs among accuracy, privacy, explainability, scalability, and robustness to adversarial manipulation.Collectively, these research avenues highlight critical opportunities to develop next generation security mechanisms that are both operationally deployable and aligned with emerging regulatory, ethical, and trust requirements in AI enabled cyber defense.