Reactive Jamming Detection Based on Hidden Markov Model
Leyi Zhang, Tianqi Mao, Chen Zhang, Zhenyu Xiao, Xiang‐Gen Xia · 2022 IEEE/CIC International Conference on Communications in China (ICCC) · 2022
Due to the strong stealthiness and capability of legal channel awareness, a reactive jamming attack is considered as a serious security concern to wireless communications. The existing reactive jamming detection schemes require prior knowledge of legal user's signal characteristics or the channel parameters, which is hardly available in practice, e.g., emergency situations and battlefield environments. To this end, this paper aims at detecting a reactive jamming without any prior information. To solve the problem, we propose a hidden-Markov-model-based (HMM-based) jamming detection method. Firstly, we model the relationship between received signal power and the state of jamming presence as a hidden Markov process. Then, the Expectation-Maximization (EM) algorithm and forward-backward recursions are applied to estimate model parameters and hidden states of jamming presence. The final decision is made according to the difference between the estimated signal statistical characteristics under jamming presence and absence. Numerical simulations demonstrate the superiority of the proposed reactive jamming detection scheme, in the absence of prior knowledge of legal signal and channel statistics.