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S. Xu, C. Hu, L. Wang and G. Zhang, “Support Vector Machines Based on K Nearest Neighbor Algorithm for Outlier Detection in WSNs,” Proceedings of the 8th Wireless Communications, Networking and Mobile Com- puting International Conference WICOM, Shanghai, 21-23 September 2012, pp. 1-4.

has been cited by the following article:

• TITLE: Research on Intrusion Detection Algorithm Based on Multi-Class SVM in Wireless Sensor Networks

  AUTHORS: Hangxia Zhou, Qian Liu, Chen Cui

  KEYWORDS: Wireless Sensor Network; Multi-Class; Network Security

  JOURNAL NAME: Communications and Network, Vol.5 No.3C, October 7, 2013

  ABSTRACT: A multi-class method is proposed based on Error Correcting Output Codes algorithm in order to get better performance of attack recognition in Wireless Sensor Networks. Aiming to enhance the accuracy of attack detection, the multi-class method is constructed with Hadamard matrix and two-class Support Vector Machines. In order to minimize the complexity of the algorithm, sparse coding method is applied in this paper. The comprehensive experimental results show that this modified multi-class method has better attack detection rate compared with other three coding algorithms, and its time efficiency is higher than Hadamard coding algorithm.