Detecting Byzantine attack in cognitive radio networks using machine learning

Sarmah, Ranjan Kumar; Taggu, Amar; Marchang, Ningrinla

doi:10.1007/s11276-020-02398-w

Cited by 20 publications

(11 citation statements)

References 33 publications

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“…In [106], several ML techniques such as SVM, Neural Network, Naive Bayes, and Ensemble classifiers were implemented to detect SSDF attacks in a CRN. The learning techniques were investigated under two experimental scenarios: (a) the training and test data were drawn from the same data-set, and (b) separate datasets were used for training and testing.…”

Section: ) Outlier Detection Methodsmentioning

confidence: 99%

When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challenges

Wang

Sun

et al. 2022

IEEE Open J. Commun. Soc.

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The exponential growth of internet connected systems has generated numerous challenges, such as spectrum shortage issues, which require efficient spectrum sharing (SS) solutions. Complicated and dynamic SS systems can be exposed to different potential security and privacy issues, requiring protection mechanisms to be adaptive, reliable, and scalable. Machine learning (ML) based methods have frequently been proposed to address those issues. In this article, we provide a comprehensive survey of the recent development of ML based SS methods, the most critical security issues, and corresponding defense mechanisms. In particular, we elaborate the state-of-the-art methodologies for improving the performance of SS communication systems for various vital aspects, including ML based cognitive radio networks (CRNs), ML based database assisted SS networks, ML based LTE-U networks, ML based ambient backscatter networks, and other ML based SS solutions. We also present security issues from the physical layer and corresponding defending strategies based on ML algorithms, including Primary User Emulation (PUE) attacks, Spectrum Sensing Data Falsification (SSDF) attacks, jamming attacks, eavesdropping attacks, and privacy issues. Finally, extensive discussions on open challenges for ML based SS are also given. This comprehensive review is intended to provide the foundation for and facilitate future studies on exploring the potential of emerging ML for coping with increasingly complex SS and their security problems.

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Section: ) Outlier Detection Methodsmentioning

confidence: 99%

When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challenges

Wang

Sun

et al. 2022

IEEE Open J. Commun. Soc.

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“…In another work, a suprathreshold stochastic resonance method was proposed to detect weak PU signals by artificially attaching noise resonance leading to signal enhancement by Q. Li and Z. Li in [11]. N. Marchang et al proposed an intrusion detection scheme based on Markov chain model in [12]- [14]. [15] improved the performance of CSS in the case of low generalized SNR by using the maximum generalized correntropy.…”

Section: Related Workmentioning

confidence: 99%

Secure and Efficient Cooperative Spectrum Sensing Against Byzantine Attack for Interweave Cognitive Radio System

2022

KSII TIIS

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Due to increasing spectrum demand for new wireless devices applications, cooperative spectrum sensing (CSS) paradigm is the most promising solution to alleviate the spectrum shortage problem. However, in the interweave cognitive radio (CR) system, the inherent nature of CSS opens a hole to Byzantine attack, thereby resulting in a significant drop of the CSS security and efficiency. In view of this, a weighted differential sequential single symbol (WD3S) algorithm based on MATLAB platform is developed to accurately identify malicious users (MUs) and benefit useful sensing information from their malicious reports in this paper. In order to achieve this, a dynamic Byzantine attack model is proposed to describe malicious behaviors for MUs in an interweave CR system. On the basis of this, a method of data transmission consistency verification is formulated to evaluate the global decision's correctness and update the trust value (TrV) of secondary users (SUs), thereby accurately identifying MUs. Then, we innovatively reuse malicious sensing information from MUs by the weight allocation scheme. In addition, considering a high spectrum usage of primary network, a sequential and differential reporting way based on a single symbol is also proposed in the process of the sensing information submission. Finally, under various Byzantine attack

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“…Machine learning methods such as SVM, neural network, Naive Bayes and Ensemble classifier are also widely used to detect SSDF attacks in CRN [37]. Sarmah R et al developed a sliding window trust model based on Bayesian inference to identify and eliminate independent cooperative SSDF attackers [38].…”

Section: Related Workmentioning

confidence: 99%

Research on SSDF Attack Detection Algorithm in Cognitive Internet of Things

Liu

Huo

et al. 2021

Preprint

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The Internet of Things (IoT) is a new paradigm for connecting various heterogeneous networks.cognitive radio (CR) adopts cooperative spectrum sensing (CSS) to realize the secondary utilization of idle spectrum by unauthorized IoT devices,so that IoT objects can effectively use spectrum resources.However, the abnormal IoT devices in the cognitive Internet of Things will disrupt the CSS process. For this attack, we propose a spectrum sensing strategy based on the weighted combining of the Hidden Markov Model. In this method, Hidden Markov Model is used to detect the probability of malicious attack of each node and report it to the fusion center (FC). FC allocates a reasonable weight value according to the evaluation of the submitted observation results to improve the accuracy of the sensing results.Simulation results show that the detection performance of spectrum sensing data forgery(SSDF) attack in cognitive Internet of Things is better than that of K rank criterion in hard combining.

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Detecting Byzantine attack in cognitive radio networks using machine learning

Cited by 20 publications

References 33 publications

When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challenges

When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challenges

Secure and Efficient Cooperative Spectrum Sensing Against Byzantine Attack for Interweave Cognitive Radio System

Research on SSDF Attack Detection Algorithm in Cognitive Internet of Things

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