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Attacker Detection for Cooperative Spectrum Sensing in Cognitive Radio Network: Using Cluster Reduction Technique

Pemma Venkata Sivadeep*, P. Venkatramana**

* PG Scholar, Communication Systems, Department of ECE, Sree Vidyanikethan Engineering College, Tirupati, India.

** Professor and Head, Department of Electronics and Communication Engineering, Sree Vidyanikethan Engineering College, Tirupati, India.

Periodicity:November - January'2015
DOI : https://doi.org/10.26634/jcs.4.1.3292

Abstract

In co-operative spectrum sensing system, secondary users co-operate among themselves, improving the cognitive radio spectrum sensing performance. The presence of two or more malicious user in the secondary users severely degrades the performance of the cognitive radio spectrum sensing system. In this paper, the author have studied the detection and elimination of the malicious users in a co-operative spectrum sensing system using clustering technique namely k-mean clustering and compared its performance with Dixon's outlier test and box plot test. The performance of the system with different outlier tests is shown by plotting the receiver operating characteristics curves. From this we can show that cluster removal method outperforms the Dixon's outlier test and box plot test for the case of two or three malicious users in a co-operative spectrum sensing system for cognitive radio network.

Keywords

Box Plot Method, Dixon's Outlier Test, K-mean Clustering, Receiver Operating Characteristics .

How to Cite this Article?

Sivadeep, P. V., and Ramana, P. V. (2015). Attacker Detection for Cooperative Spectrum Sensing in Cognitive Radio Network: Using Cluster Reduction Technique. i-manager’s Journal on Communication Engineering and Systems, 4(1), 19-24. https://doi.org/10.26634/jcs.4.1.3292

References

[1]. Federal Communications Commission, (2003). "Facilitating opportunities for flexible, efficient, and reliable spectrum use employing spectrum agile radio technologies," ET Docket No. 03-108.

[2]. Kanungo, Tapas; Mount, D.M.; Netanyahu, N.S.; Piatko, C.D.; Silverman, R.; Wu, A.Y., (2002). "An efficient kmeans clustering algorithm: analysis and implementation," Pattern Analysis and Machine Intelligence, IEEE Transactions, Vol.24, No.7, pp.881,892.

[3]. Kalamkar, S.S.; Banerjee, A.; Roychowdhury, A., (2012). "Malicious user suppression for cooperative spectrum sensing in cognitive radio networks using Dixon's outlier detection method," Communications (NCC), National Conference, Vol.1, No.5, pp.3-5.

[4]. V. Barnett and T. Lewis, (1985). “Outliers in Statistical Data”, New York, NY: John Wiley and Sons, 2nd ed.

[5]. S. Verma and A. Quiroz-Ruiz, (2006). "Critical values for six dixon tests for outliers in normal samples up to sizes 100, and applications in science and engineering," Revista Mexicana de Ciencias Geolgicas, Vol. 23, No. 2, pp. 133–161.

[6]. F. Mosteller and J. Tukey, (1977). “Data Analysis and Regression : A Second Course in Statistics”, Reading: MA: Addison-Wesley, 1st ed.

[7]. D. A. Lax, (1985). "Robust estimators of scale: finitesample performance in log-tailed symmetric distributions," J. American Statistical Association, Vol. 80, pp. 736–741.

[8]. R. McGill, J. W. Tukey, and W. A. Larsen, (1978). "Variations of box plots," The American Statistician, Vol. 32, pp. 12–16.

[9]. C.S.Hyder, B.Grebur and Li Xiao, (2012). "Defense against Spectrum Sensing Data Falsification Attacks in Cognitive Radio Networks," Security and Privacy in Computer Networks.

[10]. S. Haykin, (2005). "Cognitive radio: Brain Empowered Wireless Communications, IEEE Journal Selet. Area on Communications, Vol. 23, No. 2, pp: 201-220.

[11]. R. Chen, J. M. Park, and K. Bian, (2008). "Robust distributed spectrum sensing in cognitive radio networks," Proc. 27th IEEE Conference on Computer Communications (INFOCOM'08), (Phoenix, AZ), pp. 1876–1884.

[12]. W. Wang, H. Li., Y. L. Sun, and Z. Han, (2009). "Attackproof collaborative spectrum sensing in cognitive radio networks," Proc. 43rd Annual Conference on Information Sciences and Systems (CISS'09), (Baltimore, MD), pp. 130–134.

[13]. P. Kaligineedi, M. Khabbazian, and V. K. Bhargava, (2010). "Malicious user detection in a cognitive radio cooperative sensing system," IEEE Transactions on Wireless Communications, Vol. 9, No. 8, pp. 2488–2497.

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