An improved convolutional neural network model for intrusion detection in networks

Riaz Ullah Khan, Xiaosong Zhang, Mamoun Alazab, Rajesh Kumar

    Research output: Chapter in Book/Report/Conference proceedingConference Paper published in Proceedingspeer-review

    102 Citations (Scopus)

    Abstract

    Network intrusion detection is an important component of network security. Currently, the popular detection technology used the traditional machine learning algorithms to train the intrusion samples, so as to obtain the intrusion detection model. However, these algorithms have the disadvantage of low detection rate. Deep learning is more advanced technology that automatically extracts features from samples. In view of the fact that the accuracy of intrusion detection is not high in traditional machine learning technology, this paper proposes a network intrusion detection model based on convolutional neural network algorithm. The model can automatically extract the effective features of intrusion samples, so that the intrusion samples can be accurately classified. Experimental results on KDD99 datasets show that the proposed model can greatly improve the accuracy of intrusion detection.

    Original languageEnglish
    Title of host publicationProceedings - 2019 Cybersecurity and Cyberforensics Conference, CCC 2019
    EditorsCristina Ceballos
    Place of PublicationPiscataway, NJ
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages74-77
    Number of pages4
    Edition1
    ISBN (Electronic)9781728126005
    DOIs
    Publication statusPublished - 3 Oct 2019
    Event2019 Cybersecurity and Cyberforensics Conference, CCC 2019 - Melbourne, Australia
    Duration: 7 May 20198 May 2019

    Publication series

    NameProceedings - 2019 Cybersecurity and Cyberforensics Conference, CCC 2019

    Conference

    Conference2019 Cybersecurity and Cyberforensics Conference, CCC 2019
    Country/TerritoryAustralia
    CityMelbourne
    Period7/05/198/05/19

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