Sequential Single Voting for Cooperative Spectrum Sensing Against Byzantine Attack

Wu, Jun; Chen, Zehao; Liang, Haoyu; Chen, Ze; Zhang, Jia; Gan, Jipeng; He, Jiangtao

doi:10.1145/3585967.3585968

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…In (Gao, Zhang, et Kang 2019), Gao et al propose a CSS scheme where the cooperative efficiency is improved by using a truncated sequential likelihood ratio test (T-SPRT), where the value reached by the number of observation vectors is then truncated. In (Wu et al 2023), Wu et al propose a secure CSS scheme based on the Sequential Single Voting (SSV) rule. In this SSV, each node is assigned a trust value that determines the reliability of its reports, and a pair of thresholds is used to remove attackers and selectively reuse attacker reports in the merge.…”

Section: 1mentioning

confidence: 99%

Sequential fusion-based defence strategy against sophisticated Byzantine attacks in cognitive IoT networks

ZEMO,

Bakkali

2023

Preprint

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5G and 6G promise to be catalysts for the Internet of Things (IoT), enabling ultra-massive and mission-critical IoT. The requirements of new IoT applications and the large number of connected objects announced will exacerbate the thorny problem of spectrum scarcity in wireless communications. Cognitive radio is positioned as a revolutionary technology that promises to mitigate the problem of spectrum scarcity through more efficient use of spectrum. Integrating cognitive radio into the IoT allows objects to opportunistically access spectrum resources already allocated to another user (primary user, PU) without causing interference. Spectrum sensing allows objects to be aware of the spectrum occupancy status of the target PU. In radio environments where the PU signal is subject to multipath and shadowing effects, making spectrum sensing by a single object very difficult, cooperative spectrum sensing (CSS) has been proposed to improve detection performance. However, this cooperation between objects has opened the way to a new form of attack known as the Spectrum Sensing Data Falsification (SSDF) or Byzantine attack. In our paper, we propose a sophisticated Byzantine attack model that generalizes known simple attacks. Then we propose a new innovative and intelligent weighted sequential hypothesis testing scheme that ensures CSS security by significantly reducing the average number of samples required for global decision-making in a highly hostile IoT network. Simulation results under various attack scenarios clearly show that our security mechanism leads to better performance compared to other mechanisms such as classical WSPRT, SPRT and majority rule.

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Section: 1mentioning

confidence: 99%

Sequential fusion-based defence strategy against sophisticated Byzantine attacks in cognitive IoT networks

ZEMO,

Bakkali

2023

Preprint

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Sequential fusion‐based defense strategy against sophisticated Byzantine attacks in cognitive IoT networks

Donkeng Zemo,

Bakkali

2024

Security and Privacy

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Abstract5G and 6G promise to be catalysts for the Internet of Things (IoT), enabling ultra‐massive and mission‐critical IoT. The demands of new IoT applications and the large number of connected objects being announced will exacerbate the thorny issue of spectrum scarcity in wireless communications. Cognitive radio (CR) is a revolutionary technology that promises to mitigate the problem of spectrum scarcity through more efficient use of spectrum. Integrating CR into the IoT allows objects to opportunistically access spectrum resources already allocated to a Primary User (PU) without causing interference. Spectrum sensing (SS) allows objects to be aware of the PU's spectrum occupancy status. In radio environments where the PU signal is subject to multipath and shadowing effects that degrade the channel between the PU and objects, SS performed by a single object could be inaccurate and lead to incorrect decisions about the PU's status. Cooperative spectrum sensing (CSS) has been proposed to improve detection performance. However, this cooperation between objects has opened the way to a new form of attack known as the Spectrum Sensing Data Falsification (SSDF) or Byzantine attack. In a SSDF attack, attackers falsify their local sensing results before sharing them in the CSS. This attack is very harmful to the CSS and can lead to a loss of spectrum opportunities or interference with the PU. In this paper, from the attack point of view, a sophisticated Byzantine attack model that generalizes well the simple attack strategies has been proposed and allows an attacker to implement other attack strategies. From a defense point of view, a new and innovative Weighted Sequential Hypothesis Testing (WSPRT) scheme has been suggested. This ensures the security of the CSS while significantly reducing the average number of samples required for overall decision‐making in a very hostile IoT network. The results obtained from simulations carried out in various attacks scenarios show that the proposed secure CSS scheme requires at most six samples to detect the state of the PU without error when the proportion of attackers reaches 80%. This performance far exceeds that of other defense mechanisms such as classic WSPRT, SPRT, and majority rule, with which we have compared it under the same conditions. In general, for the classic WSPRT, SPRT, and majority rule mechanisms, the error rate starts to deteriorate at ratio 40% of attackers and the number of samples is greater than six and reaches 50.

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Gaussian Mixture Model-Based Anomaly Detection for Defense Against Byzantine Attack in Cooperative Spectrum Sensing

Parmar,

Shah,

Captain

et al. 2024

IEEE Trans. Cogn. Commun. Netw.

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Sequential Single Voting for Cooperative Spectrum Sensing Against Byzantine Attack

Cited by 3 publications

References 17 publications

Sequential fusion-based defence strategy against sophisticated Byzantine attacks in cognitive IoT networks

Sequential fusion-based defence strategy against sophisticated Byzantine attacks in cognitive IoT networks

Sequential fusion‐based defense strategy against sophisticated Byzantine attacks in cognitive IoT networks

Gaussian Mixture Model-Based Anomaly Detection for Defense Against Byzantine Attack in Cooperative Spectrum Sensing

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