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An optimized model for network intrusion detection systems in industry 4.0 using XAI based Bi-LSTM framework
Industry 4.0 enable novel business cases, such as client-specific production, real-time monitoring of process condition and progress, independent decision making and remote maintenance, to name a few. However, they are more susceptible to a broad range of cyber threats because of limited resources a...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer London
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9999327/ https://www.ncbi.nlm.nih.gov/pubmed/37155462 http://dx.doi.org/10.1007/s00521-023-08319-0 |
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author | Sivamohan, S. Sridhar, S. S. |
author_facet | Sivamohan, S. Sridhar, S. S. |
author_sort | Sivamohan, S. |
collection | PubMed |
description | Industry 4.0 enable novel business cases, such as client-specific production, real-time monitoring of process condition and progress, independent decision making and remote maintenance, to name a few. However, they are more susceptible to a broad range of cyber threats because of limited resources and heterogeneous nature. Such risks cause financial and reputational damages for businesses, well as the theft of sensitive information. The higher level of diversity in industrial network prevents the attackers from such attacks. Therefore, to efficiently detect the intrusions, a novel intrusion detection system known as Bidirectional Long Short-Term Memory based Explainable Artificial Intelligence framework (BiLSTM-XAI) is developed. Initially, the preprocessing task using data cleaning and normalization is performed to enhance the data quality for detecting network intrusions. Subsequently, the significant features are selected from the databases using the Krill herd optimization (KHO) algorithm. The proposed BiLSTM-XAI approach provides better security and privacy inside the industry networking system by detecting intrusions very precisely. In this, we utilized SHAP and LIME explainable AI algorithms to improve interpretation of prediction results. The experimental setup is made by MATLAB 2016 software using Honeypot and NSL-KDD datasets as input. The analysis result reveals that the proposed method achieves superior performance in detecting intrusions with a classification accuracy of 98.2%. |
format | Online Article Text |
id | pubmed-9999327 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer London |
record_format | MEDLINE/PubMed |
spelling | pubmed-99993272023-03-10 An optimized model for network intrusion detection systems in industry 4.0 using XAI based Bi-LSTM framework Sivamohan, S. Sridhar, S. S. Neural Comput Appl Original Article Industry 4.0 enable novel business cases, such as client-specific production, real-time monitoring of process condition and progress, independent decision making and remote maintenance, to name a few. However, they are more susceptible to a broad range of cyber threats because of limited resources and heterogeneous nature. Such risks cause financial and reputational damages for businesses, well as the theft of sensitive information. The higher level of diversity in industrial network prevents the attackers from such attacks. Therefore, to efficiently detect the intrusions, a novel intrusion detection system known as Bidirectional Long Short-Term Memory based Explainable Artificial Intelligence framework (BiLSTM-XAI) is developed. Initially, the preprocessing task using data cleaning and normalization is performed to enhance the data quality for detecting network intrusions. Subsequently, the significant features are selected from the databases using the Krill herd optimization (KHO) algorithm. The proposed BiLSTM-XAI approach provides better security and privacy inside the industry networking system by detecting intrusions very precisely. In this, we utilized SHAP and LIME explainable AI algorithms to improve interpretation of prediction results. The experimental setup is made by MATLAB 2016 software using Honeypot and NSL-KDD datasets as input. The analysis result reveals that the proposed method achieves superior performance in detecting intrusions with a classification accuracy of 98.2%. Springer London 2023-03-10 2023 /pmc/articles/PMC9999327/ /pubmed/37155462 http://dx.doi.org/10.1007/s00521-023-08319-0 Text en © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Original Article Sivamohan, S. Sridhar, S. S. An optimized model for network intrusion detection systems in industry 4.0 using XAI based Bi-LSTM framework |
title | An optimized model for network intrusion detection systems in industry 4.0 using XAI based Bi-LSTM framework |
title_full | An optimized model for network intrusion detection systems in industry 4.0 using XAI based Bi-LSTM framework |
title_fullStr | An optimized model for network intrusion detection systems in industry 4.0 using XAI based Bi-LSTM framework |
title_full_unstemmed | An optimized model for network intrusion detection systems in industry 4.0 using XAI based Bi-LSTM framework |
title_short | An optimized model for network intrusion detection systems in industry 4.0 using XAI based Bi-LSTM framework |
title_sort | optimized model for network intrusion detection systems in industry 4.0 using xai based bi-lstm framework |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9999327/ https://www.ncbi.nlm.nih.gov/pubmed/37155462 http://dx.doi.org/10.1007/s00521-023-08319-0 |
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