Multi-Layered AI-Driven Security in Wireless Ecosystems - Heena T. Shaikh; Kazi Kutubuddin Sayyad Liyakat (International Journal of Wireless Security and Networks, 2026)
The proliferation of next-generation wireless technologies, from 5G/6G networks to the pervasive Internet of Things (IoT), has birthed a hyperconnected digital ecosystem of unprecedented scale and dynamism. This interconnectedness, however, introduces a vast and volatile attack surface, rendering conventional, signature-based security paradigms fundamentally obsolete. This paper posits that the only viable defense is an offensive, self-adaptive one, predicated on the integration of artificial intelligence (AI) directly into the wireless security fabric. A multi-layered AI framework is proposed, integrating complementary machine learning (ML), deep learning (DL), and adaptive reasoning models across four orthogonal security strata: real-time threat detection, predictive risk modeling, autonomous response orchestration, and federated learning for privacy-preserving collaboration. This framework operationalizes defense-in-depth principles while enabling proactive, self-healing resilience. Our simulation, conducted across a heterogeneous 5G-IoT testbed, demonstrates a 98.7% detection rate for anomalous traffic patterns, a 92% reduction in false positives compared to heuristic models, and a proactive prediction of network vulnerability exploits with an average lead time of 4.3 hours. The findings conclude that an AI-native approach is not a mere enhancement but a fundamental re-architecting of wireless ecosystem security, transitioning from a reactive posture to a predictive, self-healing, and resilient digital immune system.
Keywords: Wireless; artificial intelligence; detection rate; false positive; multi-layered