Self-evolving cyber defense: an analytical review of AI-driven autonomous and adversarial systems

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Authors: Babatunde AbdulAzeez Alli, Taoheed Abiodun Yusuf, Jefferson Ederhion, Oluwaseyi Otunlape, Olumide Koyenikan, Taofeek Akinwumi Raheem, Ayomide Olugbade, Stephen Alaba John, Joye Ahmed Shonubi, Md. Saifur Rahman, Md. Abdullah Al Mahmud, Olaoluwa John Adeleke
+2 more Chiebonam Benjamin Nweke, Yakubu Adekunle Alli
Cited by: 1
References: 136

Abstract

Abstract The growing complexity, size, and dynamism of cyber threats have revealed inherent weaknesses of the traditional, static cybersecurity models. Attackers nowadays take advantage of artificial intelligence, automation, and adversarial learning methods to avoid detection, create new variants of attacks, and maintain long-term intrusions. In response, cybersecurity research has turned to self-evolving cyber defense systems that can engage in constant learning, autonomous decision-making, and co-evolution with intelligent attackers. This review includes a systematic synthesis of the studies on self-evolving cyber defense with a focus on the merging of artificial intelligence, autonomy, and adversarial learning. We discuss the evolution of cyber threats, machine learning and deep learning approaches for adaptive threat detection, as well as autonomous defense systems enabled by reinforcement learning and multi-agent systems. The review also explores the adversarial machine learning as a source of emerging threats and a powerful defense foundation with focus on the co-evolution of the attackers and the defenders. In addition to algorithmic views, the paper has provided an overview of system architectures, evaluation measures, ethics and legal aspects, and real-life implementation of industrial applications in critical infrastructures, military systems, financial services, smart cities, and cyber-physical environments. The major issues concerning scalability, resistance to adaptive opponents, lack of information, and the interaction of humans and AI are addressed. Lastly, the review presents directions of future research, such as entirely autonomous defense ecosystems, hybrid neuro-symbolic systems, quantum-resilient AI security, and intelligence sharing across domains. This work brings together dispersed research in various fields to offer a reference and roadmap on how to progress to the next generation of self-evolving cyber defense systems.

Fields of Study & Subjects

Adversarial Robustness in Machine Learning Network Security and Intrusion Detection Advanced Malware Detection Techniques

Publication details

Journal / source
Journal of Computer Virology and Hacking Techniques
Publisher
Springer Science and Business Media LLC
Volume
22
Issue
1
Publication date
13 July 2026
Language
EN