An Enhanced D-S Theory Cooperative Spectrum Sensing Algorithm against SSDF Attack

Han, Yi; Chen, Qiang; Wang, Jianxin

doi:10.1109/vetecs.2012.6240040

Cited by 30 publications

(33 citation statements)

References 14 publications

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“…Here, we presented four CSS schemes based on D-S evidence theory: 'D-S Cre' (Cre is the abbreviation for Credibility) represents our proposed faithworthy CSS scheme; the curve of 'D-S Men' (Men represents the author in [17]) shows the robust CSS method with mutually supportive degree presented in [17]; another enhanced scheme with similarity degree calculation proposed in [16] is shown as 'D-S Han'; and 'D-S Nhan' represents the enhanced scheme with reliability source evaluation proposed in [15]. Besides, 'OPT LIN' shows the optimal linear CSS scheme proposed in [13], and 'SINGLE' shows the spectrum sensing scenario of single SU.…”

Section: Simulation Resultsmentioning

confidence: 99%

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Faithworthy Collaborative Spectrum Sensing Based on Credibility and Evidence Theory for Cognitive Radio Networks

Zhang

et al. 2017

Symmetry

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Abstract:Cognitive radio (CR) has become a tempting technology that achieves significant improvement in spectrum utilization. To resolve the hidden terminal problem, collaborative spectrum sensing (CSS), which profits from spatial diversity, has been studied intensively in recent years. As CSS is vulnerable to the attacks launched by malicious secondary users (SUs), certain CSS security schemes based on the Dempster-Shafer theory of evidence have been proposed. Nevertheless, the available works only focus on the real-time difference of SUs, like the difference in similarity degree or SNR, to evaluate the credibility of each SU. Since the real-time difference is unilateral and sometimes inexact, the statistical information comprised in SUs' historical behaviors should not be ignored. In this paper, we propose a robust CSS method based on evidence theory and credibility calculation. It is executed in four consecutive procedures, which are basic probability assignment (BPA), holistic credibility calculation, option and amelioration of BPA and evidence combination via the Dempster-Shafer rule, respectively. Our scheme evaluates the holistic credibility of SUs from both the real-time difference and statistical sensing behavior of SUs. Moreover, considering that the transmitted data increase with the number of SUs increasing, we introduce the projection approximation approach to adjust the evidence theory to the binary hypothesis test in CSS; on this account, both the data volume to be transmitted and the workload at the data fusion center have been reduced. Malicious SUs can be distinguished from genuine ones based on their historical sensing behaviors, and SUs' real-time difference can be reserved to acquire a superior current performance. Abounding simulation results have proven that the proposed method outperforms the existing ones under the effect of different attack modes and different numbers of malicious SUs.

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Section: Simulation Resultsmentioning

confidence: 99%

“…Therefore, we can evaluate the real-time reliability of SU i based on its reports' similarity with other SUs at each sensing round. Specifically, the similarity degree of reported BPAs between SU i and SU j can be represented by the following formulation [16]:…”

Section: Real-time Reliabilitymentioning

confidence: 99%

Faithworthy Collaborative Spectrum Sensing Based on Credibility and Evidence Theory for Cognitive Radio Networks

Zhang

et al. 2017

Symmetry

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“…According to the central limit theorem, when U is large enough (e.g., U >> 10), x Ei can be well approximated as a Gaussian random variable under both hypotheses H 0 and H 1 as follows [14,16,17]:…”

Section: Spectrum Sensing Preliminariesmentioning

confidence: 99%

“…The attack model in (5) is general enough to include the existing models as special cases, via properly adjusting the attack parameters (u 2i , u 3i , p i ). For example, Always No [13], Always Yes [14,15], and Always Adverse attacks [16] can be realized when (u 2i , u 3i , p i ) is set as (u 0i , u 0i , 1), (u 1i , u 1i , 1) and (u 1i , u 0i , 1), respectively. In particular, the attack probability p i can make the proposed attack more elusory and flexible.…”

Section: Local Decisionmentioning

confidence: 99%

“…Compared to hard SSDF attack, soft SSDF attack is generally more powerful and elusory for its relatively larger value space [9][10][11][12], since malicious SUs falsify real energy observations, rather than binary decisions, to mislead the fusion center (FC). Three soft SSDF attack models have been widely adopted to test various secure sensing algorithms: Always Yes [13], Always No [14,15], and Always Adverse [16]. In Always Yes soft SSDF attack, an attacker raises its local observations by injecting a positive offset in every sensing slot.…”

Section: Introductionmentioning

confidence: 99%

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Performance analysis of probabilistic soft SSDF attack in cooperative spectrum sensing

Zhang

Ding

et al. 2014

EURASIP J. Adv. Signal Process.

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In cognitive radio networks, spectrum sensing data falsification (SSDF) attack is a crucial factor deteriorating the detection performance of cooperative spectrum sensing. In this paper, we propose and analyze a novel probabilistic soft SSDF attack model, which goes beyond the existing models for its generalization. Under this generalized SSDF attack model, we firstly obtain closed form expressions of global sensing performance at the fusion center. Then, we theoretically evaluate the performance of the proposed attack model, in terms of destructiveness and stealthiness, sequentially. Numerical simulations match the analytical results well. Last but not least, an interesting trade-off between destructiveness and stealthiness is discovered, which is a fundamental issue involved in SSDF attack, however, ignored by most of the previous studies.

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Lightweight security against combined IE and SSDF attacks in cooperative spectrum sensing for cognitive radio networks

Sucasas

Althunibat

Radwan

et al. 2015

Security Comm Networks

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Cognitive radio is envisaged as a promising solution to cope with the problem of spectrum scarcity. In cognitive radio networks, users can sense the medium and opportunistically use available frequency bands. Users can cooperate in order to increase the reliability of the sensing process, which is called cooperative spectrum sensing (CSS). However, cooperative paradigms are threatened by the behavior of malicious users. Two types of attacks represent the main threats for CSS network operation, namely, spectrum sensing data falsification (SSDF) and incumbent emulation (IE). These two types of attacks have received a considerable amount of attention in the literature, but they have always been studied separately. In this paper, we propose a novel mechanism based on lightweight cryptography that considers both SSDF and IE attacks combined in a CSS network for the first time. Lightweight cryptography, in contrast to previous techniques used (such as intrusion detection or reputation systems), provides higher resilience to such attacks when a high number of mobile malicious users exist, providing better energy efficiency. Analytical and simulation results show the outperformance of the proposed algorithm compared with previous mechanisms, in terms of lower false alarm probability (i.e., higher chance of using the free frequency bands) and hence better energy ratings. Copyright © 2015 John Wiley & Sons, Ltd.

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An Enhanced D-S Theory Cooperative Spectrum Sensing Algorithm against SSDF Attack

Cited by 30 publications

References 14 publications

Faithworthy Collaborative Spectrum Sensing Based on Credibility and Evidence Theory for Cognitive Radio Networks

Faithworthy Collaborative Spectrum Sensing Based on Credibility and Evidence Theory for Cognitive Radio Networks

Performance analysis of probabilistic soft SSDF attack in cooperative spectrum sensing

Lightweight security against combined IE and SSDF attacks in cooperative spectrum sensing for cognitive radio networks

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