Malicious User Detection in a Cognitive Radio Cooperative Sensing System

Kaligineedi, Praveen; Khabbazian, Majid; Bhargava, V.K.

doi:10.1109/twc.2010.061510.090395

Cited by 182 publications

(114 citation statements)

References 13 publications

(26 reference statements)

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“…To address the above issues, various secure CSS schemes have been proposed [5][6][7][8][9][10][11][12][13]. When simple attack patterns are adopted by only a few malicious users (MUs) in CRNs, the schemes presented in [5][6][7] can work well enough.…”

Section: Introductionmentioning

confidence: 99%

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Comprehensive Reputation-Based Security Mechanism against Dynamic SSDF Attack in Cognitive Radio Networks

Zhang

2016

Symmetry

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Abstract:Collaborative spectrum sensing (CSS) was envisioned to improve the reliability of spectrum sensing in centralized cognitive radio networks (CRNs). However, secondary users (SUs)' changeable environment and ease of compromise make CSS vulnerable to security threats, which further mislead the global decision making and degrade the overall performance. A popular attack in CSS is the called spectrum sensing data falsification (SSDF) attack. In the SSDF attack, malicious cognitive users (MUs) send false sensing results to the fusion center, which significantly degrades detection accuracy. In this paper, a comprehensive reputation-based security mechanism against dynamic SSDF attack for CRNs is proposed. In the mechanism, the reliability of SUs in collaborative sensing is measured with comprehensive reputation values in accordance with the SUs' current and historical sensing behaviors. Meanwhile a punishment strategy is presented to revise the reputation, in which a reward factor and a penalty factor are introduced to encourage SUs to engage in positive and honest sensing activities. The whole mechanism focuses on ensuring the correctness of the global decision continuously. Specifically, the proposed security scheme can effectively alleviate the effect of users' malicious behaviors on network decision making, which contributes greatly to enhancing the fairness and robustness of CRNs. Considering that the attack strategy adopted by MUs has been gradually transforming from simplicity, fixedness and singleness into complexity, dynamic and crypticity, we introduce two dynamic behavior patterns (true to false and then to true (TFT) and false to true and then to false (FTF)) to further validate the effectiveness of our proposed defense mechanism. Abundant simulation results verify the rationality and validity of our proposed mechanism.

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Section: Introductionmentioning

confidence: 99%

“…When simple attack patterns are adopted by only a few malicious users (MUs) in CRNs, the schemes presented in [5][6][7] can work well enough. The concept of applying the trust and reputation model in CRNs has also attracted interest recently [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Reputation-Based Security Mechanism against Dynamic SSDF Attack in Cognitive Radio Networks

Zhang

2016

Symmetry

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“…The third type of malicious users will send random values for sensing malfunctioning. This may increase the false alarm probability or decrease the detection probability [3]. These malicious user scenarios have posed significant challenges in the CSS networks, especially the last type one.…”

Section: Introductionmentioning

confidence: 99%

“…A robust outlier detection utilizing outlier factors and user spatial information was proposed to identify the "Always Yes" malicious users in [3]. In addition, an outlier detection scheme based on Dixon's test was proposed to detect the presence of malicious users which may randomly send true or false value of received energy to confuse the other SUs in [5].…”

Section: Introductionmentioning

confidence: 99%

Low-rank matrix completion based malicious user detection in cooperative spectrum sensing

Qin

Gao

Plumbley

et al. 2013

2013 IEEE Global Conference on Signal and Information Processing

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Abstract-In a cognitive radio (CR) system, cooperative spectrum sensing (CSS) is the key to improving sensing performance in deep fading channels. In CSS networks, signals received at the secondary users (SUs) are sent to a fusion center to make a final decision of the spectrum occupancy. In this process, the presence of malicious users sending false sensing samples can severely degrade the performance of the CSS network. In this paper, with the compressive sensing (CS) technique being implemented at each SU, we build a CSS network with double sparsity property. A new malicious user detection scheme is proposed by utilizing the adaptive outlier pursuit (AOP) based low-rank matrix completion in the CSS network. In the proposed scheme, the malicious users are removed in the process of signal recovery at the fusion center. The numerical analysis of the proposed scheme is carried out and compared with an existing malicious user detection algorithm.

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“…The approach presented in [12] requires that the fusion centre have prior knowledge of the attackers policy, which is difficult to obtain in practice. Secure collaborative spectrum sensing based on outlier detection techniques has been proposed in [13], but assumes partial knowledge of incumbent user activity and proposed a malicious user detection scheme.…”

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confidence: 99%

Robust collaborative spectrum sensing in the presence of deleterious users

Arshad

Moessner

2013

IET Communications

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Collaborative spectrum sensing has attracted significant research attention in the last few years and is widely accepted as a viable approach to improve spectrum sensing reliability. Fusing data from multiple opportunistic users (OUs) in order to produce reliable sensing results implies a reliance on the OU to provide correct information. In the presence of malfunctioning or selfish users, performance of collaborative spectrum sensing deteriorates significantly. In this article, we propose mechanisms for the detection and suppression of such deleterious opportunistic users (DOUs) for hard and soft decision fusion. More specifically, a credibility based mechanism for hard decision fusion using a beta reputation system (HDC-BR) is introduced. Our proposed method does not require knowledge of the total number of deleterious users in advance. In HDC-BR, the fusion center assigns and updates weights to each user's decisions based on an individual user credibility score which is calculated using the beta reputation system. The presence of DOUs in soft decision based collaborative spectrum sensing has even more adverse effects on system performance. We also propose a scheme for the case of soft decision fusion to detect and eliminate falsified user observations at the fusion centre using a Modified Grubbs Test; we refer to it as SDC-MG. We compare the performance of the proposed methods with malicious user detection schemes proposed in the literature as well as with the case where no DOU suppression scheme is implemented, and conclude that SDC-MG performs much better than HDC-BR in a low Signal to Noise Ratio (SNR) regime.

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Malicious User Detection in a Cognitive Radio Cooperative Sensing System

Cited by 182 publications

References 13 publications

Comprehensive Reputation-Based Security Mechanism against Dynamic SSDF Attack in Cognitive Radio Networks

Comprehensive Reputation-Based Security Mechanism against Dynamic SSDF Attack in Cognitive Radio Networks

Low-rank matrix completion based malicious user detection in cooperative spectrum sensing

Robust collaborative spectrum sensing in the presence of deleterious users

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