A novel random neural network based approach for intrusion detection systems

Ayyaz-Ul-Haq Qureshi, Hadi Larijani, Jawad Ahmad, Nhamoinesu Mtetwa

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

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    Abstract

    Computer security and privacy of user specific data is a prime concern in day to day communication. The mass use of internet connected systems has given rise to many vulnerabilities which includes attacks on smart devices. Regular occurrence of such events has made the availability of scalable Intrusion Detection System (IDS) a perilous challenge. An intelligent IDS should be able to stop the malicious activity before it destabilizes the core network and to achieve this goal we propose a novel Random Neural Network based Intrusion Detection System (RNN-IDS) in this paper. The performance is evaluated by training different numbers of input and hidden layer neurons with learning rates on benchmark NSL-KDD dataset for binary classification. To validate the feasibility of proposed scheme, results were compared with existing systems and its performance was evaluated by the detection of novel attacks while obtaining an accuracy of 94.50%.
    Original languageEnglish
    Title of host publication2018 10th Computer Science and Electronic Engineering (CEEC)
    PublisherIEEE
    Pages50-55
    Number of pages6
    ISBN (Electronic)9781538672754
    ISBN (Print)9781538672754
    DOIs
    Publication statusPublished - 28 Mar 2019

    Keywords

    • intrusion detection
    • machine learning
    • neural networks
    • NSL-KDD
    • Internet of Things security

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  • Cite this

    Qureshi, A-U-H., Larijani, H., Ahmad, J., & Mtetwa, N. (2019). A novel random neural network based approach for intrusion detection systems. In 2018 10th Computer Science and Electronic Engineering (CEEC) (pp. 50-55). IEEE. https://doi.org/10.1109/CEEC.2018.8674228