PIGNUS: a deep learning model for IDS in industrial internet-of-things
Article
Jayalaxmi, P., Saha, R., Kumar, G., Alazab, M., Conti, M. and Cheng, X. 2023. PIGNUS: a deep learning model for IDS in industrial internet-of-things. Computers and Security. 132. https://doi.org/10.1016/j.cose.2023.103315
| Type | Article |
|---|---|
| Title | PIGNUS: a deep learning model for IDS in industrial internet-of-things |
| Authors | Jayalaxmi, P., Saha, R., Kumar, G., Alazab, M., Conti, M. and Cheng, X. |
| Abstract | The heterogeneous nature of the Industrial Internet of Thing (IIoT) has a considerable impact on the development of an effective Intrusion Detection System (IDS). The proliferation of linked devices results in multiple inputs from industrial sensors. IDS faces challenges in analyzing the features of the traffic and identifying anonymous behavior. Due to the unavailability of a comprehensive feature mapping method, the present IDS solutions are non-usable to identify zero-day vulnerabilities. In this paper, we introduce the first comprehensive IDS framework that combines an efficient feature-mapping technique and cascading model to solve the above-mentioned problems. We call our proposed solution deeP learnIG model intrusioN detection in indUStrial internet-of things (PIGNUS). PIGNUS integrates Auto Encoders (AE) to select optimal features and Cascade Forward Back Propagation Neural Network (CFBPNN) for classification and attack detection. The cascading model uses interconnected links from the initial layer to the output layer and determines the normal and abnormal behavior patterns and produces a perfect classification. We execute a set of experiments on five popular IIoT datasets: gas pipeline, water storage tank, NSLKDD+, UNSW-NB15, and X-IIoTID. We compare PIGNUS to the state-of-the-art models in terms of accuracy, False Positive Ratio (FPR), precision, and recall. The results show that PIGNUS provides more than accuracy, which is better on average than the existing models. In the other parameters, PIGNUS shows improved FPR, better recall, and better in precision. Overall, PIGNUS proves its efficiency as an IDS solution for IIoTs. Thus, PIGNUS is an efficient solution for IIoTs. |
| Keywords | IoT; Industry; Security; Intrusion; Detection |
| Sustainable Development Goals | 9 Industry, innovation and infrastructure |
| Middlesex University Theme | Creativity, Culture & Enterprise |
| Publisher | Elsevier |
| Journal | Computers and Security |
| ISSN | 0167-4048 |
| Electronic | 1872-6208 |
| Publication dates | |
| Online | 02 Jun 2023 |
| Sep 2023 | |
| Publication process dates | |
| Submitted | 19 Oct 2022 |
| Accepted | 28 May 2023 |
| Deposited | 20 Aug 2024 |
| Output status | Published |
| Publisher's version | License File Access Level Open |
| Copyright Statement | © 2023 The Authors. Published by Elsevier Ltd. This paper is published under a Creative Commons license: https://creativecommons.org/licenses/by/4.0/ see article landing page: https://doi.org/10.1016/j.cose.2023.103315 |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.cose.2023.103315 |
| Web of Science identifier | WOS:001024432400001 |
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