A Comparison Based Clustering Algorithm to Counter SSDF Attack in CWSN

Chatterjee, Sukanya; Chatterjee, Pinaki Sankar

doi:10.1109/cine.2015.46

Cited by 3 publications

(2 citation statements)

References 4 publications

(4 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The identification of a large amount of the attacking nodes was enabled, and the false detection rate of the honest nodes was reduced. Chatterjee and Chatterjee studied about the denial of service attack and introduced a similarity‐based clustering of the sensing data for provide a security measure against the spectrum sensing data falsification (SSDF) attack.…”

Section: Related Workmentioning

confidence: 99%

Beamforming‐based feature extraction and RVM‐based method for attacker node classification in CRN

Senthilkumar

Priya²

2016

Int J Communication

View full text Add to dashboard Cite

Cognitive radio is a promising technology for the future wireless spectrum allocation to improve the utilization rate of the licensed bands. However, the cognitive radio network is susceptible to various attacks. Hence, there arises a need to develop a highly efficient security measure against the attacks. This paper presents a beamforming-based feature extraction and relevance vector machine (RVM)-based method for the classification of the attacker nodes in the cognitive radio network. Initially, the allocation of the Rayleigh channel is performed for the communication. The quaternary phase shift keying method is used for modulating the signals. After obtaining the modulated signal, the extraction of the beamforming-based features is performed. The RVM classifier is used for predicting the normal nodes and attacker nodes. If the node is detected as an attacker node, then communication with that node is neglected. Particle swarm optimization is applied for predicting the optimal channel, based on the beamforming feature values. Then, signal communication with the normal nodes is started. Finally, the signal is demodulated. The signal-to-noise ratio and bit-error rate values are computed to evaluate the performance of the proposed approach. The accuracy, sensitivity, and specificity of the RVM classifier method are higher than the support vector machine classifier. The proposed method achieves better performance in terms of throughput, channel sensing/probing rate, and channel access delay.KEY WORDS: beamforming-based feature extraction; bit-error rate (BER); quaternary phase shift keying (QPSK); cognitive radio network (CRN); particle swarm optimization (PSO); relevance vector machine (RVM); signal-to-noise ratio (SNR)

show abstract

Section: Related Workmentioning

confidence: 99%

Beamforming‐based feature extraction and RVM‐based method for attacker node classification in CRN

Senthilkumar

Priya²

2016

Int J Communication

View full text Add to dashboard Cite

show abstract

“…In [8], the author proposes the utility self-information and the real utility entropy of information, which extends the range of information entropy from non-negative numbers to real numbers. In [9], the author proposed a similarity-based clustering of sensing data to counter the above attack. In [10], the author proposes a reputation based clustering algorithm that does not require prior knowledge of attacker distribution or complete identification of malicious users.…”

Section: Introductionmentioning

confidence: 99%

A Credibility-Based Defense SSDF Attacks Scheme for the Expulsion of Malicious Users in Cognitive Radio

Du¹,

Fu²,

Chu³

2015

IJHIT

View full text Add to dashboard Cite

Cognitive radio (CR) can improve the utilization of the spectrum by making use of licensed spectrum in an opportunistic manner. However, the security aspects of cognitive radio networks have garnered little attention. In this paper, we identify a threat to cognitive radio networks, which we call the spectrum sensing data falsification (SSDF) attack. SSDF attack can hugely degrade the achievable detection accuracy. To counter this threat, we proposed a new method to confront the SSDF attacks by excluding malicious users is cognitive radio. In detail, the proposed scheme defense the SSDF attacks by calculating and updating the credit value of the Secondary Users (SUs), malicious users are excluded to avoid the attacks affect in cooperative spectrum sensing. Simulations results show that both the detection probability and the false alarm probability are significantly improved compared to the case when all users are by default trusted to be normal users.

show abstract

The Cluster-based Cognitive Radio Sensor Networks that are Wireless Aware of PUEA and SSDF Attacks

Thakkar¹,

Goswami

2023

2023 International Conference on Applied Intelligence and Sustainable Computing (ICAISC)

View full text Add to dashboard Cite

A Comparison Based Clustering Algorithm to Counter SSDF Attack in CWSN

Cited by 3 publications

References 4 publications

Beamforming‐based feature extraction and RVM‐based method for attacker node classification in CRN

Beamforming‐based feature extraction and RVM‐based method for attacker node classification in CRN

A Credibility-Based Defense SSDF Attacks Scheme for the Expulsion of Malicious Users in Cognitive Radio

The Cluster-based Cognitive Radio Sensor Networks that are Wireless Aware of PUEA and SSDF Attacks

Contact Info

Product

Resources

About