Browsing by Author "Lee, Seo Jin"

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    DFSGD: Machine Learning Based Intrusion Detection for Resource Constrained Devices
    (2019-12) Lee, Seo Jin; Chermak, Lounis; Richardson, Mark A.; Yoo, Paul D.; Asyhari, Taufiq
    An ever increasing number of smart and mobile devices interconnected through wireless networks such as Internet of Things (IoT) and huge sensitive network data transmitted between them has raised security and privacy issues. Intrusion detection system (IDS) is known as an effective defence system and often, machine learning (ML) and its subfield deep learning (DL) methods are used for its development. However, IoT devices have limited computational resources such as limited energy source and computational power and thus, traditional IDS that require extensive computational resource are not suitable for running on such devices. Therefore, the aim of this research is to design and develop a lightweight ML-based IDS for the resource-constrained devices. The research proposes a lightweight ML-based IDS model based on Deep Feature Learning with Linear SVM and Gradient Descent optimisation (DFSGD) 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 C4.5 wrapper. The DFSGD is trained on Aegean Wi-Fi Intrusion Dataset (AWID) to detect impersonation attack and utilises support vector machine (SVM) and gradient descent as the classifier and optimisation algorithm respectively. As one of the key contributions of this research, the features in AWID dataset utilised for the development of the model, were also investigated for its usability for further development of IDS. Finally, the DFSGD was run on Raspberry Pi to show its possible deployment on resource-constrained devices.
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    IMPACT: Impersonation attack detection via edge computing using deep auto encoder and feature abstraction
    (IEEE, 2020-04-02) Lee, Seo Jin; Yoo, Paul D.; Asyhari, A. Taufiq; Jhi, Yoonchan; Chermak, Lounis; Yeun, Chan Yeob; Taha, Kamal
    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.