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

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Author(s)Zhang, Chen
Author(s)Zhang, Leyi
Author(s)Mao, Tianqi
Author(s)Xiao, Zhenyu
Author(s)Han, Zhu
Author(s)Xia, Xiang-Gen
Date Accessioned2023-06-02T18:12:25Z
Date Available2023-06-02T18:12:25Z
Publication Date2023-02-28
Description© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This article was originally published in IEEE Transactions on Cognitive Communications and Networking. The version of record is available at: https://doi.org/10.1109/TCCN.2023.3244539
AbstractDue 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.
SponsorThis work was supported by the Defense Industrial Technology Development Program with Grant number JCKY2020601B014
CitationC. Zhang, L. Zhang, T. Mao, Z. Xiao, Z. Han and X. -G. Xia, "Detection of Stealthy Jamming for UAV-Assisted Wireless Communications: An HMM-based Method," in IEEE Transactions on Cognitive Communications and Networking, doi: 10.1109/TCCN.2023.3244539.
ISSN2332-7731
URLhttps://udspace.udel.edu/handle/19716/32819
Languageen_US
PublisherIEEE Transactions on Cognitive Communications and Networking
Keywordscommunication security
Keywordsunmanned aerial vehicle
Keywordsjamming detection
Keywordshidden Markov model
Keywordshypothesis test
TitleDetection of Stealthy Jamming for UAV-Assisted Wireless Communications: An HMM-based Method
TypeArticle
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