AI Driven Jamming in 6G Networks

Abstract

This paper investigates AI-driven jamming as an emerging security threat to sixth-generation (6G) wireless networks. Unlike conventional jamming techniques that rely on relatively fixed and predictable interference patterns, AI-driven jamming can use machine learning to sense network conditions, adapt attack strategies, and optimize interference in real time. The paper provides a technical analysis of AI-driven jamming in 6G environments, covering reinforcement learning, deep Q-networks, adaptive frequency jamming, deceptive signal imitation, and coordinated multi-agent attacks. It examines potential vulnerabilities in 6G architectures and evaluates different jamming strategies using metrics such as disruption success, detection probability, and energy efficiency. In addition, the paper proposes a layered defense framework combining physical-layer detection, adaptive signal processing, machine-learning-based anomaly detection, dynamic spectrum access, and cooperative defense mechanisms. The paper concludes by identifying open research challenges, including game-theoretic defense, formal verification, cross-layer security, and quantum-safe spectrum security. Keywords: 6G Networks, AI-Driven Jamming, Machine Learning, Reinforcement Learning, Wireless Security, Spectrum Security, Anti-Jamming, Network Security.