Muhammad Faisal, Aedah Abd Rahman, Alders Paliling, I. Dewa Made Widia, Raden Teddy Iswahyudi, Nizirwan Anwar, Anshika Sharma, Titik Khawa Abdul Rahman
This chapter provides a comprehensive analysis of machine-learning-driven security for wireless communication networks, showing how adaptive models supplant static, rule-based defenses to counter sophisticated cyber threats. Motivated by escalating traffic volume and protocol heterogeneity, it explains key algorithms, architectural patterns, and training pipelines that enable real-time anomaly detection, autonomous intrusion response, and scalable protection across diverse topologies. Data challenges including labeling scarcity, class imbalance, and concept drift are reviewed alongside adversarial robustness, interpretability, and computational limits at the edge. Case studies demonstrate practical deployments of supervised, unsupervised, and reinforcement techniques, while ethical concerns such as privacy preservation and algorithmic bias are critically assessed. The chapter concludes by mapping open research directions that position machine learning as a pivotal enabler of intelligent, resilient wireless cybersecurity. Copyright © 2026, IGI Global Scientific Publishing. Copying or distributing in print or electronic forms without written permission of IGI Global Scientific Publishing is prohibited. Use of this chapter to train generative artificial intelligence (AI) technologies is expressly prohibited. The publisher reserves all rights to license its use for generative AI training and machine learning model development.
Asia e University, Malaysia; Muhammadiyah University of Makassar, Indonesia; Universitas Sembilanbelas November, Kolaka, Indonesia; Universitas Brawijaya, Malang, Indonesia; Universitas Esa Unggul, Jakarta, Indonesia; National Institute of Technology, Raipur, India
