Analysis of Byzantine Attack Strategy for Cooperative Spectrum Sensing

Wu, Jun; Li, Pei; Chen, Yang; Tang, Jifei; Wei, Chao; Xia, Lanhua; Song, Tiecheng

doi:10.1109/lcomm.2020.2990869

Cited by 38 publications

(11 citation statements)

References 9 publications

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“…A number of research works can be found on the above concept. It has been used for cluster based reputation formulation to detect intrusion attacks in [123], generalized Byzantine attack and defence in [124,125] for the analysis of attack strategies in the absence of defence and implementing suitable defence strategy in [126], for the removal of anomalous values from the data fusion process in [127], for the removal of malicious data in heterogeneous and ultra-dense networks in [128], for implementing a Bayesian inference based sliding-window trust mechanism in [129] analysing a cost benefit trade-off in Byzantine attacks in CSS in [130] and detecting MUs in non-orthogonal multiple access based systems in [131].…”

Section: Reputation Based Mechanismsmentioning

confidence: 99%

A survey on security issues in cognitive radio based cooperative sensing

et al. 2021

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Cognitive radio based cooperative spectrum sensing (CSS) is severely affected when some secondary users maliciously attack it. Two attacks regarded as key adversaries to the success of CSS are spectrum sensing data falsification (SSDF) and primary user emulation attack (PUEA). Defending SSDF and PUEAs has received significant attention in research in the past decade globally. This paper performs a state-of-the-art comprehensive survey of the researches on defending SSDF and PUEAs. First, the preliminaries like Hypothesis testing for detecting the primary user and different models of CSS are discussed briefly. Then a categorization of the defence mechanisms for defending both the attacks has been proposed as active and passive. Active mechanisms are suitable for an immediate defence in a limited time span, while passive mechanisms are suitable for flexible CSS systems that are ready to detect the attacks over a period of time and suppress them permanently by bringing changes in their underlying operations. An in-depth tutorial on both the defence mechanisms is provided from the perspectives of the secondary users throughput and the interference to the primary user. Finally, a detailed survey on the open research problems in this area and some possible solutions has been performed. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Reputation Based Mechanismsmentioning

confidence: 99%

A survey on security issues in cognitive radio based cooperative sensing

et al. 2021

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“…is the additive white Gaussian noise of the i th IoT device, and Z i [k] and S[k] are independent of each other.h i is the channel gain of the communication between the i th IoT device and the PU. H 0 is the current frequency band is free, and H 1 is the current frequency band is occupied [41].…”

Section: Spectrum-aware Modelmentioning

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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“…A generalised attack model was used to compute the attack cost and benefit, and cost‐benefit trade‐off issues. A generalised soft Byzantine attack model was developed in [33] to examine attack strategies in the absence of any defence, in terms of the attack strength and probability. A classical trust‐value‐based CSS algorithm was used to carried attack strategies and estimate the security of Byzantine attacker.…”

Section: Related Workmentioning

confidence: 99%

Optimised Levenshtein centroid cross‐layer defence for multi‐hop cognitive radio networks

Ganesh

Kumar

Kumar³

2020

IET Communications

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Cognitive radio networks (CRN) make use of dynamic spectrum access to communicate opportunistically. Unlicensed users severely affect the spectrum sensing outcomes in CRN. Primary user emulation attack (PUEA) and spectrum sensing data falsification (SSDF) have become a paramount concern in CRNs. It is especially challenging when both masquerading (in the physical layer) and falsification (in the data link layer) occur by providing false spectrum reports. Existing methods to detect such attacks cannot be utilised in scenarios with multi-hop CRN. In this study, to mitigate attack against PUEA and SSDF, a method called optimised sensing and Levenshtein nearest centroid classification (OS-LNCC) for multi-hop CRN is presented. First, a network model for multi-hop CRN is designed. Next, a probable density optimal logical sensing model is designed to alleviate the problems related to falsification of spectrum reports. Here, the falsification of spectrum reports is overcome by exploiting dual factors, that is, probability for false alarm and probability for detection according to the departure rate of primary user (PU). With these dual factors, optimal logical sensing is made, therefore improving the throughput with minimum delay. Finally, each cognitive radio (CR) user evaluates its current sensing information to existing sensing classes through the Levenshtein distance function. Based on quantitative variables, the prediction function of each sensing class is measured using nearest centroid (NC) classifier and the sensing report is classified into either presence or absence of PU. These predictive classes are then integrated at the fusion centre so that robust mitigation against PUEA and SSDF is made. Computer simulation outputs show that OS-LNCC method performs better than the conventional methods using metrics such as sensing delay by 47%, percentage of error in prediction by 46% and throughput by 45%.

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Analysis of Byzantine Attack Strategy for Cooperative Spectrum Sensing

Cited by 38 publications

References 9 publications

A survey on security issues in cognitive radio based cooperative sensing

A survey on security issues in cognitive radio based cooperative sensing

Research on SSDF Attack Detection Algorithm in Cognitive Internet of Things

Optimised Levenshtein centroid cross‐layer defence for multi‐hop cognitive radio networks

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