ARC: Adaptive Reputation based Clustering Against Spectrum Sensing Data Falsification Attacks

Hyder, Chowdhury Sayeed; Grebur, Brendan; Xiao, Li; Ellison, Max

doi:10.1109/tmc.2013.26

Cited by 53 publications

(15 citation statements)

References 17 publications

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“…In order to reconstructed the receive signal from receiver antennas, the sampling rate should be greater than or equal to the Nyquist rate , one of two bits [19,27] . Thus, the bit-extraction rate should reach 6 10 bit/s theoretically. The strong extractor uses an SHA-256 algorithm to generate the final key based on channel response characteristics from the quantized amplitude values and quantized phase values, respectively.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…Another typical type of attack is the Spectrum Sensing Data Falsification (SSDF) attacks, in which the malicious attackers send a modified spectrum sensing result to the central combiner in a multi-node collaborative spectrum sensing strategy. Researchers have intensively studied both PUE attacks and SSDF attacks in the past few years [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

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A New Fuzzy Key Generation Method Based on PHY-Layer Fingerprints in Mobile Cognitive Radio Networks

2016

KSII TIIS

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Classical key generation is complicated to update and key distribution generally requires fixed infrastructures. In order to eliminate these restrictions researchers have focused much attention on physical-layer (PHY-layer) based key generation methods. In this paper, we present a PHY-layer fingerprints based fuzzy key generation scheme, which works to prevent primary user emulation (PUE) attacks and spectrum sensing data falsification (SSDF) attacks, with multi-node collaborative defense strategies. We also propose two algorithms, the EA algorithm and the TA algorithm, to defend against eavesdropping attacks and tampering attacks in mobile cognitive radio networks (CRNs). We give security analyses of these algorithms in both the spatial and temporal domains, and prove the upper bound of the entropy loss in theory. We present a simulation result based on a MIMO-OFDM communication system which shows that the channel response characteristics received by legitimates tend to be consistent and phase characteristics are much more robust for key generation in mobile CRNs. In addition, NIST statistical tests show that the generated key in our proposed approach is secure and reliable.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Fuzzy Key Generation Method Based on PHY-Layer Fingerprints in Mobile Cognitive Radio Networks

2016

KSII TIIS

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“…Chen et al [18] applied Modified Grubbs test and employed Conjugate Prior-based theory to detect the malicious users based on the soft fusion scheme, in which the sensing reports is taken as a stochastic process. By investigating the malicious node's manipulation of sensing result independently or collaboratively, Hyder et al [19] proposed an adaptive reputation-based clustering mechanism, which requires no prior knowledge of distribution of attacking nodes and is applicable for a wide range of attacking scenarios. Focusing on the massive attacks, Sharifi et al [20] made use of Weighted Likelihood Ratio Test to estimate the credit value of each SU.…”

Section: Related Workmentioning

confidence: 99%

An Enhanced Cooperative Spectrum Sensing Scheme Against SSDF Attack Based on Dempster-Shafer Evidence Theory for Cognitive Wireless Sensor Networks

Yao

Yuan

et al. 2020

IEEE Access

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Due to the potential applicability of spectrum sensing, cognitive wireless sensor networks have attracted plenty of interest in the research community to improve the bandwidth utilization for practical applications. To alleviate the effect of multi-path fading and resolve the problem of hidden terminal, collaborative spectrum sensing (CSS) is regarded as effective technology to obtain better sensing accuracy. However, CSS is usually vulnerable to the attack behaviors originated from malicious sensor nodes. In this paper, an enhanced cooperative spectrum sensing scheme against SSDF attack based on Dempster-Shafer evidence theory for cognitive wireless sensor networks is introduced. First, the holistic credibility of sensor nodes can be evaluated according to the real-time difference between them and the statistical sensing behaviors. Furthermore, the basic probability assignment function can be defined based on evidence theory, and the credibility of sensor nodes can be estimated. Finally, by using the weighted probability assignment for each cognitive sensor node, the fusion center can reduce the influence of malicious sensor nodes on the final decision and ensure the reliability of reports from cooperative sensor nodes. Simulation results demonstrate that the proposed method can resist SSDF attacks significantly and outperform the traditional secure schemes in aspect of sensing accuracy.

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“…Over the course of the past decades, many efforts have been made to evaluate, recognize, predict, and prevent attacks or frauds in reputation systems [8][9][10]. There are three main kinds of techniques for detecting fraud in reputation aggregation: majority rule [11], signal modeling, and trust management [12].…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Collusive Attack Detection Mechanism for Reputation Aggregation in Mobile Social Networks: A Trust Relationship Based Perspective

Zhang

Song

Yang

et al. 2016

Mobile Information Systems

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While the mechanism of reputation aggregation proves to be an effective scheme for indicating an individual’s trustworthiness and further identifying malicious ones in mobile social networks, it is vulnerable to collusive attacks from malicious nodes of collaborative frauds. To conquer the challenge of detecting collusive attacks and then identifying colluders for the reputation system in mobile social networks, a fuzzy collusive attack detection mechanism (FCADM) is proposed based on nodes’ social relationships, which comprises three parts: trust schedule, malicious node selection, and detection traversing strategy. In the first part, the trust schedule provides the calculation method of interval valued fuzzy social relationships and reputation aggregation for nodes in mobile social networks; further, a set of fuzzy valued factors, that is, item judgment factor, node malicious factor, and node similar factor, is given for evaluating the probability of collusive fraud happening and identifying single malicious nodes in the second part; and moreover, a detection traversing strategy is given based on random walk algorithm under the perspectives of fuzzy valued nodes’ trust schedules and proposed malicious factors. Finally, our empirical results and analysis show that the proposed mechanism in this paper is feasible and effective.

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ARC: Adaptive Reputation based Clustering Against Spectrum Sensing Data Falsification Attacks

Cited by 53 publications

References 17 publications

A New Fuzzy Key Generation Method Based on PHY-Layer Fingerprints in Mobile Cognitive Radio Networks

A New Fuzzy Key Generation Method Based on PHY-Layer Fingerprints in Mobile Cognitive Radio Networks

An Enhanced Cooperative Spectrum Sensing Scheme Against SSDF Attack Based on Dempster-Shafer Evidence Theory for Cognitive Wireless Sensor Networks

A Fuzzy Collusive Attack Detection Mechanism for Reputation Aggregation in Mobile Social Networks: A Trust Relationship Based Perspective

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