Bayesian Stackelberg Game for Antijamming Transmission With Incomplete Information

Jia, Luliang; Yao, Fuqiang; Sun, Youming; Niu, Yingtao; Zhu, Yimin

doi:10.1109/lcomm.2016.2598808

Cited by 106 publications

(57 citation statements)

References 11 publications

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“…(6) According to the gradient descent algorithm, the network weight θ i is updated according to (6). Although there are two CNN networks with different weights, as shown in Fig.…”

Section: Anti-jamming Communication Schemementioning

confidence: 99%

Anti-Jamming Communications Using Spectrum Waterfall: A Deep Reinforcement Learning Approach

et al. 2018

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This letter investigates the problem of anti-jamming communications in dynamic and unknown environment through on-line learning. Different from existing studies which need to know (estimate) the jamming patterns and parameters, we use the spectrum waterfall, i.e., the raw spectrum environment, directly. Firstly, to cope with the challenge of infinite state of raw spectrum information, a deep anti-jamming Q-network is constructed. Then, a deep anti-jamming reinforcement learning algorithm is proposed to obtain the optimal anti-jamming strategies. Finally, simulation results validate the the proposed approach. The proposed approach is relying only on the local observed information and does not need to estimate the jamming patterns and parameters, which implies that it can be widely used various anti-jamming scenarios.

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“…(6) According to the gradient descent algorithm, the network weight θ i is updated according to (6). Although there are two CNN networks with different weights, as shown in Fig.…”

Section: Anti-jamming Communication Schemementioning

confidence: 99%

Anti-Jamming Communications Using Spectrum Waterfall: A Deep Reinforcement Learning Approach

et al. 2018

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“…The Stackelberg game is a well-developed and appropriate method to cope with hierarchical interactions among players in anti-jamming field . Furthermore, an anti-jamming Bayesian Stackelberg game with incomplete information is proposed In Jia, Yao, Sun, Niu, and Zhu (2016).…”

Section: Resilient Control Of Wncssmentioning

confidence: 99%

A survey on the recent development of securing the networked control systems

Wang

Yang

2019

Systems Science & Control Engineering

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Recent years witness an ever-increasing research interest in the study of securing the Networked Control Systems due mainly to the trend of integrating the advanced communication technical into the traditional control system. As a result, the control system becomes prone to the cyber attacks and a number of serious security incidents of the industrial control system are reported. Firstly, we recall the most recent occurred security incident of control system, and various types of controlsystem-oriented cyber attacks are introduced. Then, a wealth of resilient control methodologies are presented which aim to securing the control system. Finally, the applications of the resilient control method to the power system is introduced.

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“…Furthermore, in [29] reinforcement learning can be applied to determine transmission powers against a jammer in a dynamic environment without knowing the underlying Game model. In [1], authors propose an anti-jamming Bayesian Stackelberg Game with incomplete information. In all the previous works on anti-jamming, the authors consider the problem transmitter-jammer during only one transmission attempt.…”

Section: Related Workmentioning

confidence: 99%

Smart Jamming Attacks in Wireless Networks During a Transmission Cycle: Stackelberg Game with Hierarchical Learning Solution

Lmater¹,

Haddad²,

Karouit³

et al. 2018

ijacsa

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Abstract-Due to the broadcast nature of the shared medium, wireless communications become more vulnerable to malicious attacks. In this paper, we tackle the problem of jamming in wireless network when the transmission of the jammer and the transmitter occur with a non-zero cost. We focus on a jammer who keeps track of the re-transmission attempts of the packet until it is dropped. Firstly, we consider a power control problem following a Nash Game model, where all players take action simultaneously. Secondly, we consider a Stackelberg Game model, in which the transmitter is the leader and the jammer is the follower. As the jammer has the ability to sense the transmission power, the transmitter adjusts its transmission power accordingly, knowing that the jammer will do so. We provide the closed-form expressions of the equilibrium strategies where both the transmitter and the jammer have a complete information. Thereafter, we consider a worst case scenario where the transmitter has an incomplete information while the jammer has a complete information. We introduce a Reinforcement Learning method, thus, the transmitter can act autonomously in a dynamic environment without knowing the above Game model. It turns out that despite the jammer ability of sensing the active channel, the transmitter can enhance its efficiency by predicting the jammer reaction according to its own strategy.

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Bayesian Stackelberg Game for Antijamming Transmission With Incomplete Information

Cited by 106 publications

References 11 publications

Anti-Jamming Communications Using Spectrum Waterfall: A Deep Reinforcement Learning Approach

Anti-Jamming Communications Using Spectrum Waterfall: A Deep Reinforcement Learning Approach

A survey on the recent development of securing the networked control systems

Smart Jamming Attacks in Wireless Networks During a Transmission Cycle: Stackelberg Game with Hierarchical Learning Solution

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