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- W2902529532 abstract "Abstract Due to the widespread diffusion of network connectivity, the demand for network security and protection against cyber-attacks is ever increasing. Intrusion detection systems (IDS) perform an essential role in today's network security. This paper proposes an IDS based on feature selection and clustering algorithm using filter and wrapper methods. Filter and wrapper methods are named feature grouping based on linear correlation coefficient (FGLCC) algorithm and cuttlefish algorithm (CFA), respectively. Decision tree is used as the classifier in the proposed method. For performance verification, the proposed method was applied on KDD Cup 99 large data sets. The results verified a high accuracy (95.03%) and detection rate (95.23%) with a low false positive rate (1.65%) compared to the existing methods in the literature." @default.
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- W2902529532 date "2019-02-01" @default.
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- W2902529532 title "Cyber intrusion detection by combined feature selection algorithm" @default.
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- W2902529532 doi "https://doi.org/10.1016/j.jisa.2018.11.007" @default.
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