6G Sensing Security: Distributed Game-Theoretic RL for Urban Beamforming and Attacker Detection

2026-07-07

AI Summary

This work addresses the security vulnerability in 6G integrated sensing and communication (ISAC) systems, where adversaries can manipulate beamforming directions to steer the base station’s mainlobe away from legitimate users, thereby inducing interference and compromising system integrity. To counter this threat, the study pioneers a dynamic interaction model between users and attackers by integrating distributed game theory with reinforcement learning. It proposes a utility-driven distributed reinforcement learning framework that jointly enables anti-jamming beamforming and proactive attacker identification. Simulation results demonstrate that the proposed approach effectively detects malicious entities in complex, dynamic urban environments while preserving both communication and sensing performance for legitimate users, significantly enhancing the security of 6G ISAC systems.

Abstract

In next-generation networks, communication systems will no longer be limited to data transmission and will be expected to acquire awareness of the surrounding environment. This leads to the concept of integrated sensing and communication (ISAC), where the same wireless infrastructure is used for both communication and environmental sensing. Thus, ISAC enables the system to transmit information efficiently and observe and interpret channel variations and user behavior. Motivated by this capability, this work focuses on detecting an active attacker in an urban environment scenario, where the attacker intentionally manipulates beamforming directions to increase interference and mislead the transmitter into allocating the main lobe of beam toward itself instead of legitimate users. We apply game-theoretic approaches to model the interaction between legitimate users and the attacker, and integrate the resulting utility-based formulation into a reinforcement learning (RL) framework. Simulation results demonstrate that the proposed method effectively addresses security challenges in dynamic 6G ISAC systems.

Problem

• 6G
• Integrated Sensing and Communication
• Beamforming
• Attacker Detection
• Security

Innovation

• Integrated Sensing and Communication (ISAC)
• Game-theoretic Reinforcement Learning
• Beamforming Security
• Attacker Detection
• 6G Networks