BIROn - Birkbeck Institutional Research Online

    IMPACT: Impersonation attack detection via edge computing using deep autoencoder and feature abstraction

    Lee, S.J. and Yoo, Paul D. and Asyhari, T.A. and Jhi, Y. and Chermak, L. and Yeun, C.Y. and Taha, K. (2020) IMPACT: Impersonation attack detection via edge computing using deep autoencoder and feature abstraction. IEEE Access 8 , pp. 65520-65529. ISSN 2169-3536.

    [img] Text
    2_A PDF of the final manuscript.pdf - Author's Accepted Manuscript
    Restricted to Repository staff only
    Available under License Creative Commons Attribution.

    Download (998kB)
    31507a.pdf - Published Version of Record
    Available under License Creative Commons Attribution.

    Download (6MB) | Preview


    An ever-increasing number of computing devices interconnected through wireless networks encapsulated in the cyber-physical-social systems and a significant amount of sensitive network data transmitted among them have raised security and privacy concerns. Intrusion detection system (IDS) is known as an effective defence mechanism and most recently machine learning (ML) methods are used for its development. However, Internet of Things (IoT) devices often have limited computational resources such as limited energy source, computational power and memory, thus, traditional ML-based IDS that require extensive computational resources are not suitable for running on such devices. This study thus is to design and develop a lightweight ML-based IDS tailored for the resource-constrained devices. Specifically, the study proposes a lightweight ML-based IDS model namely IMPACT (IMPersonation Attack deteCTion using deep auto-encoder and feature abstraction). This is based on deep feature learning with gradient-based linear Support Vector Machine (SVM) to deploy and run on resource-constrained devices by reducing the number of features through feature extraction and selection using a stacked autoencoder (SAE), mutual information (MI) and C4.8 wrapper. The IMPACT is trained on Aegean Wi-Fi Intrusion Dataset (AWID) to detect impersonation attack. Numerical results show that the proposed IMPACT achieved 98.22% accuracy with 97.64% detection rate and 1.20% false alarm rate and outperformed existing state-of-the-art benchmark models. Another key contribution of this study is the investigation of the features in AWID dataset for its usability for further development of IDS.


    Item Type: Article
    Additional Information: (c) 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
    Depositing User: Paul Yoo
    Date Deposited: 03 Apr 2020 09:38
    Last Modified: 09 Aug 2023 12:48


    Activity Overview
    6 month trend
    6 month trend

    Additional statistics are available via IRStats2.

    Archive Staff Only (login required)

    Edit/View Item Edit/View Item