Trust-Based System to Defend Against the Spectrum Sensing Data Falsification Attack in Cognitive Radio Ad Hoc Network

Ngomane, Issah; Velempini, Mthulisi; Dlamini, Sabelo

doi:10.1109/icabcd.2018.8465461

Cited by 8 publications

(3 citation statements)

References 11 publications

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“…In [24] and with the aid of the Thompson sampling technique, an online primary user emulation attack was investigated. In [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], novel algorithms were proposed by considering the energy efficiency, spectrum maintenance, bandwidth guaranteeing, robustness, complexity, deep-learning hardware/software, probabilisticity, secure fusion strategies, specific channel models e.g. Nakagami-m fading etc.…”

Section: A Related Workmentioning

confidence: 99%

Adversarial Robustness in Cognitive Radio Networks

Zamanipour¹

2022

Preprint

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When an adversary gets access to the data sample in the adversarial robustness models and can make data-dependent changes, how has the decision maker consequently, relying deeply upon the adversarially-modified data, to make statistical inference? How can the resilience and elasticity of the network be literally justified from a game theoretical viewpoint − if there exists a tool to measure the aforementioned elasticity? The principle of byzantine resilience distributed hypothesis testing (BRDHT) is considered in this paper for cognitive radio networks (CRNs) − withoutloss-of-generality, something that can be extended to any type of homogeneous or heterogeneous networks. We use the temporal rate of the 𝛼−leakage as the appropriate tool which we measure the aforementioned resilience through. We take into account the main problem from an information theoretic point of view via an exploration over the adversarial robustness of distributed hypothesis testing rules. We chiefly examine if one can write F = 𝑚𝑎 for the main problem, consequently, we define a nested bilevel − even 3-level including a hidden control-law − mean-fieldgame (MFG) realisation solving the control dynamics as well. Further discussions are also provided e.g. the synchronisation. Our novel online algorithm − which is named OBRDHT − and solution are both unique and generic over which an evaluation is finally performed by simulations.

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Section: A Related Workmentioning

confidence: 99%

Adversarial Robustness in Cognitive Radio Networks

Zamanipour¹

2022

Preprint

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“…On the other hand, the flexibility of CRNs system brings about susceptibilities that may allow an attacker to masquerade as a SU leading to a spectrum sensing data falsification (SSDF) attack [4]. In SSDF attacks, malicious SUs forward falsify local sensing results to the FC to mislead the universal decision.…”

Section: Introductionmentioning

confidence: 99%

“…Some SSDF attack detection algorithms have been proposed to detect malicious SUs in CSS [6], however, the problem in the CRN with SSDF attack is still an open issue. In [4], SSDF in CSS was solved via a trust-based system; however, this technique required prior knowledge of the PU signal. These requirements are incongruous when applying to high mobile CRN, and to such systems in which the information of the PU is not entirely known.…”

Section: Introductionmentioning

confidence: 99%

Robust Statistics Evidence Based Secure Cooperative Spectrum Sensing for Cognitive Radio Networks

Simpson

Sun

2020

2020 International Wireless Communications and Mobile Computing (IWCMC)

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Cognitive radio networks (CRNs), an assemble of smart schemes intended for permitting secondary users (SUs) to opportunistically access spectral bands vacant by primary user (PU), has been deliberated as a solution to improve spectrum utilization. Cooperative spectrum sensing (CSS) is a vital technology of CRN systems used to enhance the PU detection performance by exploiting SUs' spatial diversity, however CSS leads to spectrum sensing data falsification (SSDF), a new security threat in CR system. The SSDF by malicious users can lead to a decrease in CSS performance. In this work, we propose a CSS scheme in which the presence and absence hypotheses distribution of PU signal is estimated based on past sensing received energy data incorporating robust statistics, and the data fusion are performed according to an evidence based approach. Simulation results show that the proposed scheme can achieve a significant malicious user reduction due to the abnormality of the distribution of malicious users compared with that of other legitimate users. Furthermore, the performance of our data fusion scheme is improved by supplemented nodes' credibility weight.

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