Countermeasure for Smart Jamming Threat: A Deceptively Adversarial Attack Approach

摘要

With the development of software-defined radio (SDR) and artificial intelligence (AI), smart jammers are becoming more and more powerful, and have cognitive and intelligent capacities such as spectrum sensing, learning and reconfigurability. To cope with the AI-enabled smart jammers, this paper investigates the naive weakness of smart jamming strategies and proposes a deceptively adversarial attack (DAA) based proactively intelligent anti-jamming algorithm to ensure reliable communication for wireless communication networks (WCNs). In contrast to the existing intelligent schemes, the proposed DAA-based anti-jamming algorithm focus on minimizing total reward of the smart jammer and destroy their spectrum sensing and learning abilities by delivering a carefully designed deceptively adversarial signal. We implement the DAA-based anti-jamming algorithm in both white-box and black-box settings according to the degree of information acquisition from the smart jammers, respectively. Simulation results show that system performance in terms of anti-jamming reward, computational complexity and anti-jamming efficiency can be significantly improved compared with the existing intelligent schemes.