Exploitation Analysis of Byzantine attack for Cooperative Spectrum Sensing

Gan, Jipeng; Wu, Jun

doi:10.1109/vtc2021-fall52928.2021.9625254

Cited by 5 publications

(2 citation statements)

References 19 publications

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“…Therefore, the joint spectrum sensing and resource allocation scheme is proposed in this paper, which can be split into two subalgorithms are the selection-majority and spectrum resource allocation (SRA) algorithm. Following our previous work, 35,36 in addition to enriching and improving the system model and the exploitation of Byzantine attack, this paper also extends to spectrum resource allocation. Specifically, the main contribution of this paper can be summarized as follows.…”

Section: Our Contributionsmentioning

confidence: 97%

Joint spectrum sensing and resource allocation against byzantine attack in overlay cognitive radio networks

Gan

et al. 2022

Trans Emerging Tel Tech

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Cognitive radio (CR) technology has emerged as a viable solution to solve the dilemma of spectrum scarcity and underutilization. It allows secondary users (SUs) to opportunistically access the primary user (PU) channel. Cooperative spectrum sensing (CSS) further improves the accuracy of detecting spectrum availability by exploiting spatial diversity gain. However, the open nature of CR networks (CRNs) makes CSS process be prone to Byzantine attack, resulting in the excessive interference to the PU and the waste of spectrum resources. To this end, we propose a joint spectrum sensing and resource allocation scheme to defend against Byzantine attack. Specifically, we develop a probabilistic Byzantine attack model to characterize attack behaviors from malicious users (MUs) and evaluate its negative impact on the CSS performance. Then, the delivery evaluation mechanism is designed to evaluate the sensing performance of SUs and distinguish between normal users and MUs. Based on this, we propose a selection-majority algorithm to improve CSS performance by exploiting sensing reports from MUs. To further solve the Byzantine attack, we propose a spectrum resource allocation algorithm, which allocates corresponding spectrum resources according to the historical behavior of SUs. By reducing the resources allocated to poorly performing SUs, this scheme incentivizes them to report correct sensing results and drive MUs to stop attacking or leave CRN. Extensive simulation results corroborate the correctness and effectiveness of our theoretical analysis and show that our proposed scheme can effectively resist Byzantine attack.

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Section: Our Contributionsmentioning

confidence: 97%

Joint spectrum sensing and resource allocation against byzantine attack in overlay cognitive radio networks

Gan

et al. 2022

Trans Emerging Tel Tech

Self Cite

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“…Feng et al proposed a flexible intermittent SSDF (ISSDF) attack and corresponding countermeasures in References 16,17, but only focused on the static attack and ignored the change of the attack probability. By using Bayesian risk maximization and analyzing the sensing results, in References 18–21, by suspiciousness judgment and by the attacker's attack parameter identification respectively, which improves the stability of detection of MU. But they didn't completely solve the problem of large‐scale attacks.…”

Section: Motivation and Contributionmentioning

confidence: 99%

Dynamic sliding window‐cooperative spectrum sensing against massive SSDF attack in interweave cognitive internet of things

Zhu,

Wu,

et al. 2024

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To improve spectrum utilization, cognitive radio (CR) enables unauthorized internet of thing (IoT) devices to opportunistically access the channel underutilized by the primary user (PU) in a cognitive IoT (CIoT). To this end, cooperative spectrum sensing (CSS) plays a crucial role in CR technology, which leverages the spatial diversity of corroborative IoT devices to accurately detect the PU signal. However, this open cooperative paradigm may suffer from spectrum sensing data falsification (SSDF) attack in which malicious IoT devices intentionally mislead the fusion center (FC) by providing false sensing results to make an incorrect global decision regarding the PU status. To effectively characterize the attack behavior of malicious IoT devices, we propose a massive SSDF attack model described by the attack cycle and malicious ratio within a sensing period to characterize the malicious behaviors. Additionally, we introduce a delivery evaluation mechanism and propose a dynamic sliding window‐CSS (DSW‐CSS) to mitigate the impact of massive SSDF attack. Moreover, we introduce a sequential reporting mechanism to further reduce the number of samples required by the global decision‐making of the FC. Finally, simulation results show the flexibility and aggressiveness of the proposed attack model and demonstrate the correctness and effectiveness of DSW‐CSS.

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Securing consensus in fractional-order multi-agent systems: Algebraic approaches against Byzantine attacks

Zhong,

Khan,

Javeed

et al. 2024

Heliyon

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Exploitation Analysis of Byzantine attack for Cooperative Spectrum Sensing

Cited by 5 publications

References 19 publications

Joint spectrum sensing and resource allocation against byzantine attack in overlay cognitive radio networks

Joint spectrum sensing and resource allocation against byzantine attack in overlay cognitive radio networks

Dynamic sliding window‐cooperative spectrum sensing against massive SSDF attack in interweave cognitive internet of things

Securing consensus in fractional-order multi-agent systems: Algebraic approaches against Byzantine attacks

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