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• W1657901570 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." @default.
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• W1657901570 date "2014-01-01" @default.
• W1657901570 modified "2023-09-26" @default.
• W1657901570 title "kENFIS: kNN-based evolving neuro-fuzzy inference system for computer worms detection" @default.
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• W1657901570 doi "https://doi.org/10.3233/ifs-130868" @default.
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