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    ARTICLE

    Real-Time Network Intrusion Prevention System Using Incremental Feature Generation

    Yeongje Uhm1, Wooguil Pak2,*

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1631-1648, 2022, DOI:10.32604/cmc.2022.019667 - 07 September 2021

    Abstract Security measures are urgently required to mitigate the recent rapid increase in network security attacks. Although methods employing machine learning have been researched and developed to detect various network attacks effectively, these are passive approaches that cannot protect the network from attacks, but detect them after the end of the session. Since such passive approaches cannot provide fundamental security solutions, we propose an active approach that can prevent further damage by detecting and blocking attacks in real time before the session ends. The proposed technology uses a two-level classifier structure: the first-stage classifier supports real-time More >

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