Deceiving Reactive Jamming in Dynamic Wireless Sensor Networks: A Deep Reinforcement Learning Based Approach

Chen Zhang; Tianqi Mao; Zhenyu Xiao; Ruiqi Liu; Xiang Gen Xia

doi:10.1109/GLOBECOM54140.2023.10437052

Deceiving Reactive Jamming in Dynamic Wireless Sensor Networks: A Deep Reinforcement Learning Based Approach

Chen Zhang, Tianqi Mao, Zhenyu Xiao, Ruiqi Liu, Xiang Gen Xia

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

Abstract

A reactive jamming attack, which performs spec-trum jamming only during legal signal transmission based on the knowledge of sensor behaviors, poses a significant threat to wireless sensing networks (WSNs). In this paper, a novel deceiving approach is proposed for defending reactive jamming in dynamic WSNs. Specifically, when the maximum transmission power is given, we first formulate the anti-jamming process as an optimization problem to maximize the average received power while eliminating the effects of the jamming attack. Then the interaction between reactive jamming and legitimate sensors is modeled with the Markov decision process (MDP). Finally, a deep Q network (DQN) based jamming deceiving method is proposed to solve the formulated optimization problem. Simulation results show that the proposed anti-jamming scheme can converge quickly and is superior to the classical counterparts in terms of the mean of received signal power.

Original language	English
Title of host publication	GLOBECOM 2023 - 2023 IEEE Global Communications Conference
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4455-4460
Number of pages	6
ISBN (Electronic)	9798350310900
DOIs	https://doi.org/10.1109/GLOBECOM54140.2023.10437052
Publication status	Published - 2023
Externally published	Yes
Event	2023 IEEE Global Communications Conference, GLOBECOM 2023 - Kuala Lumpur, Malaysia Duration: 4 Dec 2023 → 8 Dec 2023

Publication series

Name	Proceedings - IEEE Global Communications Conference, GLOBECOM
ISSN (Print)	2334-0983
ISSN (Electronic)	2576-6813

Conference

Conference	2023 IEEE Global Communications Conference, GLOBECOM 2023
Country/Territory	Malaysia
City	Kuala Lumpur
Period	4/12/23 → 8/12/23

Keywords

Reactive jamming
deep Q network (DQN)
deep reinforcement learning
jamming deceiving
wireless sensor network (WSN)

Access to Document

10.1109/GLOBECOM54140.2023.10437052

Cite this

Zhang, C., Mao, T., Xiao, Z., Liu, R., & Xia, X. G. (2023). Deceiving Reactive Jamming in Dynamic Wireless Sensor Networks: A Deep Reinforcement Learning Based Approach. In GLOBECOM 2023 - 2023 IEEE Global Communications Conference (pp. 4455-4460). (Proceedings - IEEE Global Communications Conference, GLOBECOM). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GLOBECOM54140.2023.10437052

@inproceedings{912a19b197dd478290f5c7848db2d21c,

title = "Deceiving Reactive Jamming in Dynamic Wireless Sensor Networks: A Deep Reinforcement Learning Based Approach",

abstract = "A reactive jamming attack, which performs spec-trum jamming only during legal signal transmission based on the knowledge of sensor behaviors, poses a significant threat to wireless sensing networks (WSNs). In this paper, a novel deceiving approach is proposed for defending reactive jamming in dynamic WSNs. Specifically, when the maximum transmission power is given, we first formulate the anti-jamming process as an optimization problem to maximize the average received power while eliminating the effects of the jamming attack. Then the interaction between reactive jamming and legitimate sensors is modeled with the Markov decision process (MDP). Finally, a deep Q network (DQN) based jamming deceiving method is proposed to solve the formulated optimization problem. Simulation results show that the proposed anti-jamming scheme can converge quickly and is superior to the classical counterparts in terms of the mean of received signal power.",

keywords = "Reactive jamming, deep Q network (DQN), deep reinforcement learning, jamming deceiving, wireless sensor network (WSN)",

author = "Chen Zhang and Tianqi Mao and Zhenyu Xiao and Ruiqi Liu and Xia, \{Xiang Gen\}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE Global Communications Conference, GLOBECOM 2023 ; Conference date: 04-12-2023 Through 08-12-2023",

year = "2023",

doi = "10.1109/GLOBECOM54140.2023.10437052",

language = "English",

series = "Proceedings - IEEE Global Communications Conference, GLOBECOM",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Zhang, C, Mao, T, Xiao, Z, Liu, R & Xia, XG 2023, Deceiving Reactive Jamming in Dynamic Wireless Sensor Networks: A Deep Reinforcement Learning Based Approach. in GLOBECOM 2023 - 2023 IEEE Global Communications Conference. Proceedings - IEEE Global Communications Conference, GLOBECOM, Institute of Electrical and Electronics Engineers Inc., pp. 4455-4460, 2023 IEEE Global Communications Conference, GLOBECOM 2023, Kuala Lumpur, Malaysia, 4/12/23. https://doi.org/10.1109/GLOBECOM54140.2023.10437052

Deceiving Reactive Jamming in Dynamic Wireless Sensor Networks: A Deep Reinforcement Learning Based Approach. / Zhang, Chen; Mao, Tianqi; Xiao, Zhenyu et al.
GLOBECOM 2023 - 2023 IEEE Global Communications Conference. Institute of Electrical and Electronics Engineers Inc., 2023. p. 4455-4460 (Proceedings - IEEE Global Communications Conference, GLOBECOM).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T2 - 2023 IEEE Global Communications Conference, GLOBECOM 2023

AU - Zhang, Chen

AU - Mao, Tianqi

AU - Xiao, Zhenyu

AU - Liu, Ruiqi

AU - Xia, Xiang Gen

PY - 2023

Y1 - 2023

N2 - A reactive jamming attack, which performs spec-trum jamming only during legal signal transmission based on the knowledge of sensor behaviors, poses a significant threat to wireless sensing networks (WSNs). In this paper, a novel deceiving approach is proposed for defending reactive jamming in dynamic WSNs. Specifically, when the maximum transmission power is given, we first formulate the anti-jamming process as an optimization problem to maximize the average received power while eliminating the effects of the jamming attack. Then the interaction between reactive jamming and legitimate sensors is modeled with the Markov decision process (MDP). Finally, a deep Q network (DQN) based jamming deceiving method is proposed to solve the formulated optimization problem. Simulation results show that the proposed anti-jamming scheme can converge quickly and is superior to the classical counterparts in terms of the mean of received signal power.

AB - A reactive jamming attack, which performs spec-trum jamming only during legal signal transmission based on the knowledge of sensor behaviors, poses a significant threat to wireless sensing networks (WSNs). In this paper, a novel deceiving approach is proposed for defending reactive jamming in dynamic WSNs. Specifically, when the maximum transmission power is given, we first formulate the anti-jamming process as an optimization problem to maximize the average received power while eliminating the effects of the jamming attack. Then the interaction between reactive jamming and legitimate sensors is modeled with the Markov decision process (MDP). Finally, a deep Q network (DQN) based jamming deceiving method is proposed to solve the formulated optimization problem. Simulation results show that the proposed anti-jamming scheme can converge quickly and is superior to the classical counterparts in terms of the mean of received signal power.

KW - Reactive jamming

KW - deep Q network (DQN)

KW - deep reinforcement learning

KW - jamming deceiving

KW - wireless sensor network (WSN)

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Zhang C, Mao T, Xiao Z, Liu R, Xia XG. Deceiving Reactive Jamming in Dynamic Wireless Sensor Networks: A Deep Reinforcement Learning Based Approach. In GLOBECOM 2023 - 2023 IEEE Global Communications Conference. Institute of Electrical and Electronics Engineers Inc. 2023. p. 4455-4460. (Proceedings - IEEE Global Communications Conference, GLOBECOM). doi: 10.1109/GLOBECOM54140.2023.10437052

Deceiving Reactive Jamming in Dynamic Wireless Sensor Networks: A Deep Reinforcement Learning Based Approach

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