In today`s world the wireless sensor network has great significant in application like defense surveillance, patient health monitoring, traffic control etc. As WSN utilize radio frequencies so there is threat of interference in network. These threats also include distributed denial of service in which the messages that are sent over the network may be attacked by unauthorized user. It would harm the confidentiality of the network user and the services of network. There are various algorithm that are utilized to detect Sybil attack in WSN but these schemes only stress on prevention of attack after it is occurred. This would leads to the loss of data and more consumption of limited network resources. So in this work we introduce a new algorithm that is based on clustering based KNN along with Euclidean distance. It would detect earlier the Sybil attack in WSN and prevent the data loss. The parameters like throughput, energy consumption etc are utilized to analyze the performance of this technique.

KNN, WSN, Sybil detection

[1] C. Science and K. Mangalore, “A Two-tier Network based Intrusion Detection System Architecture using Machine Learning Approach,” pp. 42–47, 2016.
[2] P. Singh and A. Tiwari, “An Efficient Approach for Intrusion Detection in Reduced Features of KDD99 Using ID3 and Classification with KNNGA,” Proc. - 2015 2nd IEEE Int. Conf. Adv. Comput. Commun. Eng. ICACCE 2015, pp. 445–452, 2015.
[3] K. J. Chabathula, C. D. Jaidhar, and M. A. Ajay Kumara, “Comparative study of Principal Component Analysis based Intrusion Detection approach using machine learning algorithms,” pp. 1–6, 2015.
[4] H. Haddad Pajouh, R. Javidan, R. Khayami, D. Ali, and K.-K. R. Choo, “A Two-layer Dimension Reduction and Two-tier Classification Model for Anomaly-Based Intrusion Detection in IoT Backbone Networks,” IEEE Trans. Emerg. Top. Comput., vol. 6750, no. c, pp. 1–1, 2016.
[5] A. R. Onik, N. F. Haq, and W. Mustahin, “Cross-breed type Bayesian network based intrusion detection system (CBNIDS),” 2015 18th Int. Conf. Comput. Inf. Technol., pp. 407–412, 2015.
[6] Y. Canbay and S. Sagiroglu, “A Hybrid Method for Intrusion Detection,” 2015 IEEE 14th Int. Conf. Mach. Learn. Appl., pp. 156–161, 2015.
[7] M. Xie and J. Hu, “Evaluating host-based anomaly detection systems: A preliminary analysis of ADFA-LD,” Proc. 2013 6th Int. Congr. Image Signal Process. CISP 2013, vol. 3, no. Cisp, pp. 1711–1716, 2013.
[8] C. Huijun, S. Hong, and Z. Hong, “Early recognition of Internet service flow,” Proc. - 2013 Wirel. Opt. Commun. Conf. WOCC 2013, pp. 464–468, 2013.
[9] S. Behrozinia, R. Azmi, M. R. Keyvanpour, and B. Pishgoo, “Biological inspired anomaly detection based on danger theory,” IKT 2013 - 2013 5th Conf. Inf. Knowl. Technol., pp. 102–106, 2013.
[10] A. Daneshpazhouh and A. Sami, “Semi-supervised outlier detection with only positive and unlabeled data based on fuzzy clustering,” 5th Conf. Inf. Knowl. Technol., pp. 344–348, 2013.
[11] T. Weiming and C. Hongzhi, “An Improved Feature Selection Algorithm Based on MAHALANOBIS Distance for Networl < Intrusion Detection,” pp. 69–73, 2013.
[12] S. Gopal, Y. Yang, K. Salomatin, and J. Carbonell, “Sctatistical learning for file-type identification,” Proc. - 10th Int. Conf. Mach. Learn. Appl. ICMLA 2011, vol. 1, no. DiiD, pp. 68–73, 2011.
[13] P. M. Mafra, V. Moll, J. Da Silva Fraga, and A. O. Santin, “Octopus-IIDS: An anomaly based intelligent intrusion detection system,” Proc. - IEEE Symp. Comput. Commun., pp. 405–410, 2010.
[14] H. Yu, P. P. K. Chan, W. W. Y. Ng, and D. S. Yeung, “Apply randomization in KNN to make the adversary harder to attack the classifier,” 2010 Int. Conf. Mach. Learn. Cybern. ICMLC 2010, vol. 1, no. July, pp. 179–183, 2010.
[15] Z. Wang et al., “Detecting Malicious Server Based on Server-to-Server Realation Graph,” 2016 IEEE First Int. Conf. Data Sci. Cybersp., pp. 698–702, 2016.