BeamSecure-AI: AI-Driven Beam-Level Attack Detection in mmWave RAN

Millimeter-wave radio access networks have a high level of security risks due to the vulnerability of having security threats at the beam level as hackers can exploit this by breaking network integrity and user privacy. This paper proposes BeamSecure-AI, an artificial intelligence-based framework that allows locating beam-level attacks and overcoming them in mmWave RAN networks in real-time. The proposed system combines deep reinforcement learning and explainable AI modules to enable it to dynamically detect threats and be transparent about the operations of the decision-making processes. We mathematically formulate the dynamic beam alignment patterns covering the multi-dimensional feature extraction through space, time, and spectral space. Experimental results validate the effectiveness of the proposed method across a range of attack scenarios, where significantly higher improvement in detection rates (96.7%) and response latency of 42.5 ms, with false-positive rates below ≤2.3%, are observed as compared to other methods. The framework can detect complex attacks such as beam stealing, jamming, and spoofing while maintaining low false-positive rates and consistent performance across urban, suburban, and rural deployment scenarios.