A Study on AI-Driven Security Concern in Wireless Ecosystem
Keywords:
AI, AI-Driven Security, Wireless ecosystem, Cyberattack, Accuracy, Precision, Recall, F1 scoreAbstract
The rapid convergence of artificial intelligence (AI) with the next-generation wireless fabric—5G, WiFi 6/7, and emerging terahertz
links—has turned the radio spectrum into a dynamic, data-rich battlefield. AI-driven security mechanisms (e.g., intelligent intrusion detection, autonomous spectrum allocation, and selfhealing protocols) promise unprecedented resilience, yet they also introduce a new class of systemic risks that are rarely captured by traditional threat models. This study surveys the landscape of AI-enabled attacks (adversarial signal crafting, model poisoning, and covert inference) and the cascade effects they trigger across heterogeneous wireless nodes, edge-cloud continuums, and IoT swarms. By coupling stochastic game theory with a multilayered threat tree analysis, we quantify the probability impact surface of AI-mediated breaches under realistic deployment constraints
(limited power, latency budgets, and heterogeneous hardware). Our simulations reveal a counterintuitive “dual-use” paradox: the very AI algorithms designed to harden the network can, when subverted, amplify the attack surface by up to 73% in dense urban microcells. The findings underscore three pivotal insights: (1) securitybydesign must embed provenanceaware model pipelines; (2) continuous, federated verification of AI decisions is indispensable for maintaining trust at the edge; and (3) regulatory sandboxes are required to benchmark AIdriven defenses against evolving adversarial playbooks. The abstract concludes that without a holistic, AIconscious security framework, the wireless ecosystem risks becoming a selfoptimizing conduit for sophisticated cyberphysical threats.
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