Combined Approach to SSDF-Attacks Mitigation in Cognitive Radio Networks

Morozov, M. Yu.; Perfilov, O. Yu.; Malyavina, N. V.; Teryokhin, R. V.; Chernova, I. V.

doi:10.1109/ieeeconf48371.2020.9078600

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…SSDF attack defence is an essential topic in CR-IoT security research. Among the existing CSS algorithms, some scholars consider preventing SSDF attacks from the historical propagation path of messages [11], or improving spectrum awareness from the perspective of reputation [12], [13], etc. Even though some scholars use clustering algorithms to cluster energy data, they have not taken further consideration [20], and have not yet seen the analysis of energy sensing data.…”

Section: B Motivationmentioning

confidence: 99%

“…[12] develops a sliding window trust model based on Bayesian inference, which can identify and eliminate independent and cooperative probabilistic SSDF attackers without prior knowledge of attack behavior. [13] proposes a combination method that combines the reputation method with q-out-of-m fusion rules. [14] proposes an enhanced cooperative spectrum sensing scheme against SSDF attacks based on evidence theory.…”

Section: A Related Workmentioning

confidence: 99%

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SSDF attack detection algorithm based on fog computing in cognitive radio-based Internet of Things

Liu¹,

He²,

Nie³

et al. 2023

Preprint

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In cognitive radio-based Internet of Things (CRIoT), security attacks of malicious devices disrupt the spectrum sensing process. The most common attack method, Spectrum Sensing Data Falsiﬁcation (SSDF) attack, attempts to make the Fusion Center (FC) misjudge the existence of the primary user (PU) by sending false energy sensing data. We propose an SSDF attack defense algorithm to deal with this security threat, which adopts fog computing architecture. FC performs Meanshift clustering, Clustering data ﬁltering, and data evaluation on the sensing data sent by CR-IoT devices to allocate appropriate weights, obtain more appropriate fusion energy and achieve the goal of accurately detecting the presence of PU. Simulation results show that in three different attack scenarios, compared with other algorithms, the proposed defence algorithm has a higher detection probability and can resist SSDF attacks more effectively.

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Section: B Motivationmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

SSDF attack detection algorithm based on fog computing in cognitive radio-based Internet of Things

Liu¹,

He²,

Nie³

et al. 2023

Preprint

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“…According to SU's current and historical perception behavior, the reliability of SU in cooperative perception is measured by comprehensive reputation value, and a penalty strategy is proposed to modify reputation [24].Ming Zhou and others proposed a cooperative spectrum sensing scheme based on CRN Bayesian Reputation Model. The key idea is to treat cooperation as a service evaluation process, and SU's reputation reflects their quality of service [25]. M. Yul.…”

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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