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International Journal of Wireless Security and Networks Review Article

Multi-Layered AI-Driven Security in Wireless Ecosystems

  1. Heena T. Shaikh Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur
  2. Kazi Kutubuddin Sayyad Liyakat Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur

Abstract

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

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