Multi-Agent Reinforcement Learning Based Cognitive Anti-Jamming

Aref, Mohamed A.; Jayaweera, Sudharman K.; Machuzak, Stephen

doi:10.1109/wcnc.2017.7925694

Cited by 101 publications

(72 citation statements)

References 13 publications

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“…Then, user n broadcasts its channel. • Independent Q-earning [14]: Each user adopts a standard Q-learning method. The coordination among users is not considered, and other users are treated as part of its environment.…”

Section: Numerical Results and Discussionmentioning

confidence: 99%

“…Based on the Q-learning method, the anti-jamming decisionmaking problem in single-user scenarios were investigated in [11]- [13]. Then, the authors in [14]- [16] extended it to the multi-user scenarios, and they resorted to the Markov game framework [17], which is the extension of the Markov decision process and can characterize the relationship among multiple users. Moreover, the corresponding multi-user reinforcement learning anti-jamming algorithm was designed.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the corresponding multi-user reinforcement learning anti-jamming algorithm was designed. However, each user employed a standard Q-learning method in [14]- [16], and the coordination among users was not considered.…”

Section: Introductionmentioning

confidence: 99%

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A Collaborative Multi-Agent Reinforcement Learning Anti-Jamming Algorithm in Wireless Networks

Yao

Jia

2019

IEEE Wireless Commun. Lett.

130

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In this letter, we investigate the anti-jamming defense problem in multi-user scenarios, where the coordination among users is taken into consideration. The Markov game framework is employed to model and analyze the anti-jamming defense problem, and a collaborative multi-agent anti-jamming algorithm (CMAA) is proposed to obtain the optimal antijamming strategy. In sweep jamming scenarios, on the one hand, the proposed CMAA can tackle the external malicious jamming. On the other hand, it can effectively cope with the mutual interference among users. Simulation results show that the proposed CMAA is superior to both sensing based method and independent Q-learning method, and has the highest normalized rate.

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Section: Numerical Results and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Collaborative Multi-Agent Reinforcement Learning Anti-Jamming Algorithm in Wireless Networks

Yao

Jia

2019

IEEE Wireless Commun. Lett.

130

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“…One of the first wireless security issues that apply reinforcement learning techniques is anti-jamming communications [8], [10]- [14], showing that a transmitter can use RL algorithms such as Q-learning to optimize its transmit power and channel selection in some simplified communication scenarios, such as very few number of feasible actions and possible states, without being aware of the network model and the jamming model. As summarized in Table I, the RL techniques have also been used in spoofing detection [6], [13], smart attacks [13] and malware detection [9].…”

Section: Rl-based Mec Security Solutionsmentioning

confidence: 99%

Security in Mobile Edge Caching with Reinforcement Learning

Xiao

Wan

Dai

et al. 2018

IEEE Wireless Commun.

223

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Mobile edge computing usually uses cache to support multimedia contents in 5G mobile Internet to reduce the computing overhead and latency. Mobile edge caching (MEC) systems are vulnerable to various attacks such as denial of service attacks and rogue edge attacks. This article investigates the attack models in MEC systems, focusing on both the mobile offloading and the caching procedures. In this paper, we propose security solutions that apply reinforcement learning (RL) techniques to provide secure offloading to the edge nodes against jamming attacks. We also present light-weight authentication and secure collaborative caching schemes to protect data privacy. We evaluate the performance of the RL-based security solution for mobile edge caching and discuss the challenges that need to be addressed in the future.

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“…Fortunately, many approaches have also been proposed to learn how to act in an unknown communication environment. The classical theory of reinforcement learning (RL), in which an agent learns and adapts its strategy by using the feedback of its actions that have been used in the past, has received much attention. Specifically, this theory learns an optimal strategy by repeatedly interacting with the environment.…”

Section: Introductionmentioning

confidence: 99%

Apprenticeship learning in cognitive jamming

ZhuanSun

Yang

Hui

2019

Optim Control Appl Methods

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In an electronic warfare-type scenario with a partially known environment, the advantages of trial and error in the reinforcement learning algorithm adopted by a cognitive jammer can disrupt communications between transmitter-receiver pairs adaptively and optimally. However, complete information regarding the Markov decision process and its thousands of interactions seem to be unreliable, especially in applications that require timeliness, such as cognitive jamming.Faced with uncooperative or hostile transmitter-receiver pairs, it is a challenging control problem for the jammer to select the proper actions to realize communication denials. This paper addresses apprenticeship learning in cognitive jamming, where inverse reinforcement learning is used by the jammer to acquire skilled behaviors from expert demonstrations. Without a known prior reward function, the jammer can obtain high jamming performance under the guidance of the given expert strategy. More specifically, the number of iterations needed in apprenticeship learning is much less than that needed in Q learning, which is a vital advantage in a fast-changing environment. Numerous results demonstrate that it is feasible and realistic to use apprenticeship learning to learn the jamming strategy since its performance meets or exceeds those of existing methods. KEYWORDScognitive jamming, inverse reinforcement learning, jamming strategy, Markov decision process, reinforcement learning Optim Control Appl Meth. 2019;40:647-658.wileyonlinelibrary.com/journal/oca

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Multi-Agent Reinforcement Learning Based Cognitive Anti-Jamming

Cited by 101 publications

References 13 publications

A Collaborative Multi-Agent Reinforcement Learning Anti-Jamming Algorithm in Wireless Networks

A Collaborative Multi-Agent Reinforcement Learning Anti-Jamming Algorithm in Wireless Networks

Security in Mobile Edge Caching with Reinforcement Learning

Apprenticeship learning in cognitive jamming

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