Mitigating RF jamming attacks at the physical layer with machine learning

Abstract

Abstract Wireless communication devices must be protected from malicious threats, including active jamming attacks, due to the widespread use of wireless systems throughout our every‐day lives. Jamming mitigation techniques are predominately evaluated through simulation or with hardware for very specific jamming conditions. In this paper, an experimental software defined radio‐based RF jamming mitigation platform which performs online jammer classification and leverages reconfigurable beam‐steering antennas at the physical layer is introduced. A ray‐tracing emulation system is presented and validated to enable hardware‐in‐the‐loop jamming experiments of complex outdoor and mobile site‐specific scenarios. Random forests classifiers are trained based on over‐the‐air collected data and integrated into the platform. The mitigation system is evaluated for both over‐the‐air and ray‐tracing emulated environments. The experimental results highlight the benefit of using the jamming mitigation system in the presence of active jamming attacks.