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PIGNUS: A Deep Learning Model for IDS in Industrial Internet-of-Things
Computers & Security, Volume: 132, Start page: 103315
Swansea University Author:
Dr Cheng Cheng
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DOI (Published version): 10.1016/j.cose.2023.103315
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...
Published in: | Computers & Security |
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ISSN: | 0167-4048 |
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Elsevier BV
2023
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URI: | https://cronfa.swan.ac.uk/Record/cronfa63596 |
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v2 63596 2023-06-06 PIGNUS: A Deep Learning Model for IDS in Industrial Internet-of-Things 11ddf61c123b99e59b00fa1479367582 0000-0003-0371-9646 Dr Cheng Cheng Dr Cheng Cheng true false 2023-06-06 SCS 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 95% accuracy, which is 25% better on average than the existing models. In the other parameters, PIGNUS shows 20% improved FPR, 10% better recall, and 10% better in precision. Overall, PIGNUS proves its efficiency as an IDS solution for IIoTs. Thus, PIGNUS is an efficient solution for IIoTs. Journal Article Computers & Security 132 103315 Elsevier BV 0167-4048 IoT, Industry, Security, Intrusion, Detection 30 9 2023 2023-09-30 10.1016/j.cose.2023.103315 http://dx.doi.org/10.1016/j.cose.2023.103315 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University This work was supported by the European Commission under the Horizon Europe Programme, as part of the project LAZARUS (https://lazarus-he.eu/) (Grant Agreement No. 101070303). 2023-07-18T13:08:57.1570206 2023-06-06T14:35:05.5385529 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science P.L.S. Jayalaxmi 1 Rahul Saha 2 Gulshan Kumar 3 Mamoun Alazab 4 Mauro Conti 0000-0002-3612-1934 5 Dr Cheng Cheng 0000-0003-0371-9646 6 Under embargo Under embargo 2023-06-07T08:55:59.0208843 Output 2580610 application/pdf Accepted Manuscript true 2024-06-02T00:00:00.0000000 Distributed under the terms of a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (CC BY-NC-ND 4.0). true eng http://creativecommons.org/licenses/by-nc-nd/4.0/ |
title |
PIGNUS: A Deep Learning Model for IDS in Industrial Internet-of-Things |
spellingShingle |
PIGNUS: A Deep Learning Model for IDS in Industrial Internet-of-Things Dr Cheng Cheng |
title_short |
PIGNUS: A Deep Learning Model for IDS in Industrial Internet-of-Things |
title_full |
PIGNUS: A Deep Learning Model for IDS in Industrial Internet-of-Things |
title_fullStr |
PIGNUS: A Deep Learning Model for IDS in Industrial Internet-of-Things |
title_full_unstemmed |
PIGNUS: A Deep Learning Model for IDS in Industrial Internet-of-Things |
title_sort |
PIGNUS: A Deep Learning Model for IDS in Industrial Internet-of-Things |
author_id_str_mv |
11ddf61c123b99e59b00fa1479367582 |
author_id_fullname_str_mv |
11ddf61c123b99e59b00fa1479367582_***_Dr Cheng Cheng |
author |
Dr Cheng Cheng |
author2 |
P.L.S. Jayalaxmi Rahul Saha Gulshan Kumar Mamoun Alazab Mauro Conti Dr Cheng Cheng |
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Computers & Security |
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132 |
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103315 |
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Swansea University |
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0167-4048 |
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10.1016/j.cose.2023.103315 |
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Elsevier BV |
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Faculty of Science and Engineering |
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url |
http://dx.doi.org/10.1016/j.cose.2023.103315 |
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description |
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 95% accuracy, which is 25% better on average than the existing models. In the other parameters, PIGNUS shows 20% improved FPR, 10% better recall, and 10% better in precision. Overall, PIGNUS proves its efficiency as an IDS solution for IIoTs. Thus, PIGNUS is an efficient solution for IIoTs. |
published_date |
2023-09-30T13:08:53Z |
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1771760132596695040 |
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11.012678 |