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

    18 Citations (Scopus)
    211 Downloads (Pure)


    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)
    Number of pages6
    ISBN (Electronic)9781538672754
    ISBN (Print)9781538672754
    Publication statusPublished - 28 Mar 2019
    Event10th Computer Science and Electronic Engineering Conference - Essex, United Kingdom
    Duration: 19 Sep 201821 Sep 2018


    Conference10th Computer Science and Electronic Engineering Conference
    Abbreviated titleCEEC’18
    Country/TerritoryUnited Kingdom


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


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