IMPACT: Impersonation Attack Detection via Edge Computing Using Deep Autoencoder and Feature Abstraction

Lee, Seo Jin and Yoo, Paul D. and Asyhari, A. Taufiq and Jhi, Yoonchan and Chermak, Lounis and Yeun, Chan Yeob and Taha, Kamal (2020) IMPACT: Impersonation Attack Detection via Edge Computing Using Deep Autoencoder and Feature Abstraction. IEEE Access, 8. pp. 65520-65529. ISSN 2169-3536

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Abstract

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
Identification Number: https://doi.org/10.1109/ACCESS.2020.2985089
Date: 2 April 2020
Uncontrolled Keywords: IoT security, intrusion detection, feature engineering, mutual information, machine learning, edge computing
Subjects: G400 Computer Science
G700 Artificial Intelligence
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Taufiq Asyhari
Date Deposited: 09 Jun 2020 14:35
Last Modified: 09 Jun 2020 14:35
URI: http://www.open-access.bcu.ac.uk/id/eprint/9328

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