Securing Cooperative Spectrum Sensing Against Collusive False Feedback Attack in Cognitive Radio Networks

Feng, Jingyu; Li, Shaoping; Lv, Shaoqing; Wang, Honggang; Fu, Anmin

doi:10.1109/tvt.2018.2841362

Cited by 31 publications

(10 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…But, CSSDF attackers may be easily detected with an abnormality detection algorithm by analyzing their highest similarities, if they launch SSDF in the static manner. Except for DSSDF and CSSDF attack, CFF attack is also found in our previous publication [21]. Since the feedback data from initiator SUs are generally unchecked, one of CFF attackers can disguise as an initiator SU who sends the feedback in accordance with the sensing data of their conspirators who play the role of cooperating SUs, resulting in promoting their conspirators' trust value quickly.…”

Section: Dc-ssdf Attack Overviewmentioning

confidence: 91%

“…Since the feedback data from initiator SUs are generally unchecked, one of CFF attackers can disguise as an initiator SU who sends the feedback in accordance with the sensing data of their conspirators who play the role of cooperating SUs, resulting in promoting their conspirators' trust value quickly. A two-level defense scheme called FeedGuard from the design ideas of feedback trust and I-C frequency correlation analysis is proposed in [21] to defend against CFF attack.…”

Section: Dc-ssdf Attack Overviewmentioning

confidence: 99%

See 1 more Smart Citation

Securing Cooperative Spectrum Sensing against DC-SSDF Attack Using Trust Fluctuation Clustering Analysis in Cognitive Radio Networks

Zhao

Feng

2019

Wireless Communications and Mobile Computing

Self Cite

View full text Add to dashboard Cite

Cooperative spectrum sensing (CSS) has been recognized as a forceful approach to promote the utilization of spectrum bands. Nevertheless, all secondary users (SU) are assumed as honest in CSS, thus giving opportunities for attackers to launch the spectrum sensing data falsification (SSDF) attack. To defend against such attack, many efforts have been made to trust mechanism. In this paper, we argue that securing CSS with only trust mechanism is not enough and report the description of dynamic-collusive SSDF attack (DC-SSDF attack). To escape the detection of trust mechanism, DC-SSDF attackers can maintain high trust by submitting true sensing data dynamically and then fake sensing data in the collaborative manner to increase their attack strength. Noting that the resonance phenomenon may appear in the trust value curve of DC-SSDF attackers, a defense scheme called TFCA is proposed from the design idea of trust fluctuation clustering analysis to suppress DC-SSDF attack. In the TFCA scheme, the decreasing property of trust value in the resonance phenomenon is adopted to measure the similarity distance between two attackers. Based on the similarity distance computation, the binary clustering algorithm is designed by electing initial binary samples to identify DC-SSDF attackers. Finally, trust mechanism can be perfected by TFCA to correct DC-SSDF attackers’ trust value. Simulation results show that our TFCA scheme can improve the accuracy of trust value calculation, thus reducing the strength of DC-SSDF attack successfully.

show abstract

Section: Dc-ssdf Attack Overviewmentioning

confidence: 91%

Section: Dc-ssdf Attack Overviewmentioning

confidence: 99%

Securing Cooperative Spectrum Sensing against DC-SSDF Attack Using Trust Fluctuation Clustering Analysis in Cognitive Radio Networks

Zhao

Feng

2019

Wireless Communications and Mobile Computing

Self Cite

View full text Add to dashboard Cite

show abstract

“…The presence of MUs may severely degrade the detection performance of CSS system [21]. There are two purposes for MUs to attack the CSS system, which are to destroy the spectrum sensing system and obtain its own benefits, respectively [22], [23].…”

Section: B Attack Modelmentioning

confidence: 99%

Clustering Algorithm-Based Data Fusion Scheme for Robust Cooperative Spectrum Sensing

et al. 2020

View full text Add to dashboard Cite

In a centralized cooperative spectrum sensing (CSS) system, it is vulnerable to malicious users (MUs) sending fraudulent sensing data, which can severely degrade the performance of CSS system. To solve this problem, we propose sensing data fusion schemes based on K-medoids and Mean-shift clustering algorithms to resist the MUs sending fraudulent sensing data in this paper. The cognitive users (CUs) send their local energy vector (EVs) to the fusion center which fuses these EVs as an EV with robustness by the proposed data fusion method. Specifically, this method takes a Medoids of all EVs as an initial value and searches for a high-density EV by iteratively as a representative statistical feature which is robust to malicious EVs from MUs. It does not need to distinguish MUs from CUs in the whole CSS process and considers constraints imposed by the CSS system such as the lack of information of PU and the number of MUs. Furthermore, we propose a global decision framework based on fast K-medoids or Mean-shift clustering algorithm, which is unaware of the distributions of primary user (PU) signal and environment noise. It is worth noting that this framework can avoid the derivation of threshold. The simulation results reflect the robustness of our proposed CSS scheme. INDEX TERMS Cognitive radio, robust cooperative spectrum sensing, sensing data fusion, K-medoids clustering algorithm, Mean-shift clustering algorithm.

show abstract

“…In [23], Jaglan et al introduced artificial neural network (ANN) at the FC thereby achieving significant improvement in detection performance and reduction in false alarm rate as compared to conventional schemes. Feng et al reported the collusive SSDF attack method and proposed a two-level (the request-level and feedback-level) and XDA defence scheme from the perspective of XOR distance analysis to defend against this attack in [24,25], respectively. Khan et al in [26] proposed a double adaptive thresholding technique in order to differentiate legitimate users from doubtful and malicious users.…”

Section: Related Workmentioning

confidence: 99%

Reuse of Byzantine data in cooperative spectrum sensing using sequential detection

Song

et al. 2020

IET Communications

View full text Add to dashboard Cite

Securing Cooperative Spectrum Sensing Against Collusive False Feedback Attack in Cognitive Radio Networks

Cited by 31 publications

References 15 publications

Securing Cooperative Spectrum Sensing against DC-SSDF Attack Using Trust Fluctuation Clustering Analysis in Cognitive Radio Networks

Securing Cooperative Spectrum Sensing against DC-SSDF Attack Using Trust Fluctuation Clustering Analysis in Cognitive Radio Networks

Clustering Algorithm-Based Data Fusion Scheme for Robust Cooperative Spectrum Sensing

Reuse of Byzantine data in cooperative spectrum sensing using sequential detection

Contact Info

Product

Resources

About