Abstract:
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.
