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DTL-IDS: An optimized Intrusion Detection Framework using Deep Transfer Learning and Genetic Algorithm

Latif, Shahid; Boulila, Wadii; Koubaa, Anis; Zou, Zhuo; Ahmad, Jawad

Authors

Shahid Latif

Wadii Boulila

Anis Koubaa

Zhuo Zou



Abstract

In the dynamic field of the Industrial Internet of Things (IIoT), the networks are increasingly vulnerable to a diverse range of cyberattacks. This vulnerability necessitates the development of advanced intrusion detection systems (IDSs). Addressing this need, our research contributes to the existing cybersecurity literature by introducing an optimized Intrusion Detection System based on Deep Transfer Learning (DTL), specifically tailored for heterogeneous IIoT networks. Our framework employs a tri-layer architectural approach that synergistically integrates Convolutional Neural Networks (CNNs), Genetic Algorithms (GA), and bootstrap aggregation ensemble techniques. The methodology is executed in three critical stages: First, we convert a state-of-the-art cybersecurity dataset, Edge_IIoTset, into image data, thereby facilitating CNN-based analytics. Second, GA is utilized to fine-tune the hyperparameters of each base learning model, enhancing the model’s adaptability and performance. Finally, the outputs of the top-performing models are amalgamated using ensemble techniques, bolstering the robustness of the IDS. Through rigorous evaluation protocols, our framework demonstrated exceptional performance, reliably achieving a 100% attack detection accuracy rate. This result establishes our framework as highly effective against 14 distinct types of cyberattacks. The findings bear significant implications for the ongoing development of secure, efficient, and adaptive IDS solutions in the complex landscape of IIoT networks.

Journal Article Type Article
Acceptance Date Nov 2, 2023
Online Publication Date Nov 16, 2023
Publication Date 2024-01
Deposit Date Nov 21, 2023
Publicly Available Date Nov 21, 2023
Print ISSN 1084-8045
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 221
Article Number 103784
Pages 103784
DOI https://doi.org/10.1016/j.jnca.2023.103784
Keywords Cybersecurity, Genetic Algorithm, IIoT, Intrusion Detection, Transfer learning
Public URL http://researchrepository.napier.ac.uk/Output/3389195

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