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

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dc.contributor.authorLee, Seo Jin
dc.contributor.authorYoo, Paul D.
dc.contributor.authorAsyhari, A. Taufiq
dc.contributor.authorJhi, Yoonchan
dc.contributor.authorChermak, Lounis
dc.contributor.authorYeun, Chan Yeob
dc.contributor.authorTaha, Kamal
dc.date.accessioned2020-06-19T16:11:44Z
dc.date.available2020-06-19T16:11:44Z
dc.date.freetoread2020-06-19
dc.date.issued2020-04-02
dc.description.abstractAn 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.en_UK
dc.identifier.citationLee SJ, Yoo PD, Asyhari AT, et al., (2020) IMPACT: Impersonation attack detection via edge computing using deep auto encoder and feature abstraction. IEEE Access, Volume 8, pp. 65520-65529en_UK
dc.identifier.cris27289733
dc.identifier.issn2169-3536
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2020.2985089
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/15508
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectImage edge detectionen_UK
dc.subjectComputational modelingen_UK
dc.subjectEdge computingen_UK
dc.subjectSecurityen_UK
dc.subjectNeuronsen_UK
dc.subjectFeature extractionen_UK
dc.titleIMPACT: Impersonation attack detection via edge computing using deep auto encoder and feature abstractionen_UK
dc.typeArticleen_UK

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