Detection of Stealthy Jamming for UAV-Assisted Wireless Communications: An HMM-Based Method

Abstract

Due to the high mobility, low cost and high robustness of line-of-sight (LoS) channels, unmanned aerial vehicles (UAVs) have begun to play an important role in assisting wireless communications. However, the broadcasting nature of wireless communication networks makes the electromagnetic spectrum vulnerable to jamming attacks. To ensure communication security, this paper investigates the jamming detection issue for UAV-assisted wireless communications. Different from the existing works, we consider detection of stealthy jamming with no prior knowledge of legitimate users or channel statistics, which makes the detection more challenging. To solve this problem, we design a hidden Markov model (HMM) based jamming detection (HBJD) method. First, we process the received signals with a sliding window to calculate the logarithmic received energy and use HMM to model the signal transmission under a jamming attack. Specifically, the spectrum state and logarithmic received energy are modeled as the hidden state and observable variable of HMM. Then, the Expectation-Maximization (EM) algorithm is applied to estimate the parameters of HMM. With the estimated parameters, the spectrum state of each logarithmic received energy sample can be decided according to the maximum posterior probability (MAP) criterion. Finally, we design the test statistics and derive the threshold based on the estimated HMM parameters for the final decision. Simulation results demonstrate the superiority of the proposed solution for the detection of stealthy jamming without prior knowledge of legitimate users or the channel statistics.