kENFIS: kNN-based evolving neuro-fuzzy inference system for computer worms detection

  • Authors:

  • A. Shubair;Sureswaran Ramadass;Altyeb Altaher Altyeb

  • Affiliations:

  • Department of Educational Technology, Sultan Qaboos University, Muscat, Oman;NAV6 Center of Excellence, Universiti Sains Malaysia USM, Penang, Malaysia;NAV6 Center of Excellence, Universiti Sains Malaysia USM, Penang, Malaysia

  • Venue:
  • Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
  • Year:
  • 2014

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Abstract

This paper presents a new kNN-based evolving neuro-fuzzy inference system kENFIS. The main function of kENFIS is to detect computer worms which possess a constant threat to Internet and have caused a significant damage to business recently. However, kENFIS can be applied to solve complex real-world problems that demand fuzzy rule-based systems able to adapt their parameters and ultimately evolve their rule base. kENFIS partitions the input space into clusters by using a new designed kNN-based evolving fuzzy clustering method kEFCM and organizes the rule base using Takagi-Sugeno method. The evolving operation is performed by incremental supervised learning. It integrates the simplicity of k-nearest neighbors kNN algorithm with the accuracy of least-square method LSM to building up the knowledge-base and learning with a few training examples. The performance of kENFIS has been evaluated and compared with some existing well-known algorithms. Also, its ability to detect worms on-line was tested. The evaluation results demonstrate that kENFIS can be effectively applied in worm detection as well as in other classification problems.