Optimal Joining Strategies in Cognitive Radio Networks Under Primary User Emulation Attacks

Li, Kaili; Wang, Jinting

doi:10.1109/access.2019.2957435

Cited by 13 publications

(9 citation statements)

References 20 publications

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“…The results shown in Figs. 14,15,16,17,18,19,20,21 have been plotted to accomplish the same. Please note that all the results are for the final values of the considered algorithms.…”

Section: Discussion On the Performance Of The Exhaustive Search Mechanismmentioning

confidence: 99%

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DQN Learning Based Defense Against Smart Primary User Emulation Attacks in Cooperative Sensing Systems

Shrivastava

Chen²,

Wang

2021

IEEE Access

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The last two decades have seen Cognitive Radio (CR) technology as an efficient mechanism to combat spectrum scarcity. Cooperative spectrum sensing (CSS) further enhances its efficiency. However, primary user emulation attack (PUEA) can affect the key purpose of CSS, by changing its fundamental model. This necessitates optimization of the CSS system facing a PUEA. In this paper, such optimization has been performed with the help of Deep Q-Network (DQN) learning. A smart-PUEA strategy, where the attack are made intelligently during the absence of the primary user (PU) signal, has been considered for investigation. First, the presence of a PUEA is modelled with the Markov chain. Then, the suppression of the damages done by the PUEA on the SU throughput has been performed in two CSS architectures: the decision fusion CSS and the data fusion CSS. In both the CSS architectures, the application of DQN learning is carried out by finding an optimal policy with the help of an action-value function. The optimal policy gives the optimal decision threshold and the optimal sensing time. As the data sets for the considered system are large, a multi-layered network is used for approximating the action-value function estimator. The reward is received in terms of maximized SU throughput. The performance assessment is done with an exhaustive search mechanism and beamforming based CSS. It is shown that the proposed algorithms outperform the existing algorithms in the achievable sum-rate and number of iterations for convergence respectively.

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“…The results shown in Figs. 14,15,16,17,18,19,20,21 have been plotted to accomplish the same. Please note that all the results are for the final values of the considered algorithms.…”

Section: Discussion On the Performance Of The Exhaustive Search Mechanismmentioning

confidence: 99%

“…In [13], a spectrum access function based framework has been proposed for the SU to access the PU spectrum in a manner which reduces it's probability of miss-detection. The authors in [14] perform a game theory based study of a system facing PUEAs. It is shown that CSS works better than the individual SU systems for PUEA avoidance.…”

Section: Introductionmentioning

confidence: 99%

DQN Learning Based Defense Against Smart Primary User Emulation Attacks in Cooperative Sensing Systems

Shrivastava

Chen²,

Wang

2021

IEEE Access

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“…Li and Wang [21] developed the queueing-gametheory for analyzing the behavior of the SU. Here, the CRN was analyzed under two types of misbehavior users such as Malicious Misbehavior Users (MMUs) and Selfish Misbehavior Users (SMUs).…”

Section: Literature Surveymentioning

confidence: 99%

Detection and Prevention of Primary User Emulation Attack in Cognitive Radio Networks Using Secure Hash Algorithm

VasanthaReddy¹,

Lingareddy²

2021

IJIES

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Cognitive radio network is enormously used to improve the spectrum utilization by enabling the dynamic spectrum access. The performance of the cognitive radio network is affected due to the denial of service caused by the primary user emulation attack. This primary user emulation attack restricts the spectrum access opportunity of secondary users. In this research, the integration of Secure Hash Algorithm-1 (SHA-1) and neural network (i.e., soft computing method) is proposed to avoid the primary user emulation attack. The Received Signal Strength (RSS) and Direction of Arrival (DoA) are used to achieve the localization between the primary and secondary users. The main objective of the proposed method is to minimize the loss ratio during the communication process. The performance of the RSS-DoA-SHA method is analyzed in terms of routing overhead, End-to-End Delay (EED) and loss ratio. The proposed RSS-DoA-SHA method is compared with two existing methods such as RSS-Angle of Arrival (AOA) method and Hybrid Routing Algorithm-Symmetric Key Cryptography (HRA-SKC) method to evaluate the performance measures. The end to end delay of the proposed method for 500 PU is 1ms, it is less when compared to the RSS-AOA method.

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“…Therefore, ensuring security is of paramount importance to the successful deployment of cognitive radio networks. More explicitly, jamming attacks, Denial of Service (DoS) attacks [11], Primary User Emulation Attack (PUEA) [12]- [14], Spectrum Sensing Data Falsification Attack (SSDFA) [15], exploitation of common control channel security [16] and collaborative jamming [17] are well known attacks in cognitive radio networks. However, our major concern is to combat jamming attacks in cognitive radio networks.…”

Section: Introductionmentioning

confidence: 99%

“…More specifically, the Q value is the estimation of the expected sum of the discounted payoff [8]. Hence, an SU can consider the Q value in a bimatrix game at stage k as the expected sum of the discounted payoffs given in (14). Given the Q function Q(s k , a k ), the SU can find the value of the game from:…”

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confidence: 99%

Anti-Jamming Game to Combat Intelligent Jamming for Cognitive Radio Networks

Ibrahim¹,

Qureshi

et al. 2021

IEEE Access

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Cognitive Radio (CR) provides a promising solution to the spectrum scarcity problem in the dense wireless networks, where the sensing ability of cognitive users helps acquire knowledge of the environment. However, cognitive users are vulnerable to different types of attacks, due to its shared medium. In particular, Jamming is considered as one of the most challenging security threat in CR networks. In jamming, an attacker jams the communication by transmitting a high power noise signal in the vicinity of the targeted node. The jammer could be an intelligent entity that is capable of exploiting the dynamics of the environment. In this work, we provide a machine-learning-based anti-jamming technique for CR networks to avoid a hostile jammer, where both the jamming and anti-jamming processes are formulated based on the Markov game framework. In our framework, secondary users avoid the jammer by maximizing its payoff function using an online, model-free reinforcement learning technique called Q-learning. We consider a realistic mathematical model, where the channel conditions are time varying and differ from one sub-channel to another, as in practical scenarios. Simulation results show that our proposed approach outperforms existing approaches to combat jamming over a wide range of scenarios.

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Optimal Joining Strategies in Cognitive Radio Networks Under Primary User Emulation Attacks

Cited by 13 publications

References 20 publications

DQN Learning Based Defense Against Smart Primary User Emulation Attacks in Cooperative Sensing Systems

DQN Learning Based Defense Against Smart Primary User Emulation Attacks in Cooperative Sensing Systems

Detection and Prevention of Primary User Emulation Attack in Cognitive Radio Networks Using Secure Hash Algorithm

Anti-Jamming Game to Combat Intelligent Jamming for Cognitive Radio Networks

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