HIDM: Hybrid Intrusion Detection Model for Industry 4.0 Networks Using an Optimized CNN-LSTM with Transfer Learning
<p dir="ltr">Industrial automation systems are undergoing a revolutionary change with the use of Internet-connected operating equipment and the adoption of cutting-edge advanced technology such as AI, IoT, cloud computing, and deep learning within business organizations. These innova...
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2023
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| _version_ | 1864513507207151616 |
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| author | Umesh Kumar Lilhore (17727684) |
| author2 | Poongodi Manoharan (17727687) Sarita Simaiya (17727693) Roobaea Alroobaea (8698965) Majed Alsafyani (17727696) Abdullah M. Baqasah (17542077) Surjeet Dalal (4906894) Ashish Sharma (319838) Kaamran Raahemifar (707645) |
| author2_role | author author author author author author author author |
| author_facet | Umesh Kumar Lilhore (17727684) Poongodi Manoharan (17727687) Sarita Simaiya (17727693) Roobaea Alroobaea (8698965) Majed Alsafyani (17727696) Abdullah M. Baqasah (17542077) Surjeet Dalal (4906894) Ashish Sharma (319838) Kaamran Raahemifar (707645) |
| author_role | author |
| dc.creator.none.fl_str_mv | Umesh Kumar Lilhore (17727684) Poongodi Manoharan (17727687) Sarita Simaiya (17727693) Roobaea Alroobaea (8698965) Majed Alsafyani (17727696) Abdullah M. Baqasah (17542077) Surjeet Dalal (4906894) Ashish Sharma (319838) Kaamran Raahemifar (707645) |
| dc.date.none.fl_str_mv | 2023-09-13T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3390/s23187856 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/HIDM_Hybrid_Intrusion_Detection_Model_for_Industry_4_0_Networks_Using_an_Optimized_CNN-LSTM_with_Transfer_Learning/26808631 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electrical engineering Information and computing sciences Artificial intelligence Cybersecurity and privacy Machine learning Industry 4.0 cyber security deep learning optimized CNN LSTM transfer learning GWO |
| dc.title.none.fl_str_mv | HIDM: Hybrid Intrusion Detection Model for Industry 4.0 Networks Using an Optimized CNN-LSTM with Transfer Learning |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Industrial automation systems are undergoing a revolutionary change with the use of Internet-connected operating equipment and the adoption of cutting-edge advanced technology such as AI, IoT, cloud computing, and deep learning within business organizations. These innovative and additional solutions are facilitating Industry 4.0. However, the emergence of these technological advances and the quality solutions that they enable will also introduce unique security challenges whose consequence needs to be identified. This research presents a hybrid intrusion detection model (HIDM) that uses OCNN-LSTM and transfer learning (TL) for Industry 4.0. The proposed model utilizes an optimized CNN by using enhanced parameters of the CNN via the grey wolf optimizer (GWO) method, which fine-tunes the CNN parameters and helps to improve the model’s prediction accuracy. The transfer learning model helps to train the model, and it transfers the knowledge to the OCNN-LSTM model. The TL method enhances the training process, acquiring the necessary knowledge from the OCNN-LSTM model and utilizing it in each next cycle, which helps to improve detection accuracy. To measure the performance of the proposed model, we conducted a multi-class classification analysis on various online industrial IDS datasets, i.e., ToN-IoT and UNW-NB15. We have conducted two experiments for these two datasets, and various performance-measuring parameters, i.e., precision, F-measure, recall, accuracy, and detection rate, were calculated for the OCNN-LSTM model with and without TL and also for the CNN and LSTM models. For the ToN-IoT dataset, the OCNN-LSTM with TL model achieved a precision of 92.7%; for the UNW-NB15 dataset, the precision was 94.25%, which is higher than OCNN-LSTM without TL.</p><h2>Other Information</h2><p dir="ltr">Published in: Sensors<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/s23187856" target="_blank">https://dx.doi.org/10.3390/s23187856</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_8a3f008a2e95415aea47d441e7f6a323 |
| identifier_str_mv | 10.3390/s23187856 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26808631 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | HIDM: Hybrid Intrusion Detection Model for Industry 4.0 Networks Using an Optimized CNN-LSTM with Transfer LearningUmesh Kumar Lilhore (17727684)Poongodi Manoharan (17727687)Sarita Simaiya (17727693)Roobaea Alroobaea (8698965)Majed Alsafyani (17727696)Abdullah M. Baqasah (17542077)Surjeet Dalal (4906894)Ashish Sharma (319838)Kaamran Raahemifar (707645)EngineeringElectrical engineeringInformation and computing sciencesArtificial intelligenceCybersecurity and privacyMachine learningIndustry 4.0cyber securitydeep learningoptimized CNNLSTMtransfer learningGWO<p dir="ltr">Industrial automation systems are undergoing a revolutionary change with the use of Internet-connected operating equipment and the adoption of cutting-edge advanced technology such as AI, IoT, cloud computing, and deep learning within business organizations. These innovative and additional solutions are facilitating Industry 4.0. However, the emergence of these technological advances and the quality solutions that they enable will also introduce unique security challenges whose consequence needs to be identified. This research presents a hybrid intrusion detection model (HIDM) that uses OCNN-LSTM and transfer learning (TL) for Industry 4.0. The proposed model utilizes an optimized CNN by using enhanced parameters of the CNN via the grey wolf optimizer (GWO) method, which fine-tunes the CNN parameters and helps to improve the model’s prediction accuracy. The transfer learning model helps to train the model, and it transfers the knowledge to the OCNN-LSTM model. The TL method enhances the training process, acquiring the necessary knowledge from the OCNN-LSTM model and utilizing it in each next cycle, which helps to improve detection accuracy. To measure the performance of the proposed model, we conducted a multi-class classification analysis on various online industrial IDS datasets, i.e., ToN-IoT and UNW-NB15. We have conducted two experiments for these two datasets, and various performance-measuring parameters, i.e., precision, F-measure, recall, accuracy, and detection rate, were calculated for the OCNN-LSTM model with and without TL and also for the CNN and LSTM models. For the ToN-IoT dataset, the OCNN-LSTM with TL model achieved a precision of 92.7%; for the UNW-NB15 dataset, the precision was 94.25%, which is higher than OCNN-LSTM without TL.</p><h2>Other Information</h2><p dir="ltr">Published in: Sensors<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/s23187856" target="_blank">https://dx.doi.org/10.3390/s23187856</a></p>2023-09-13T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/s23187856https://figshare.com/articles/journal_contribution/HIDM_Hybrid_Intrusion_Detection_Model_for_Industry_4_0_Networks_Using_an_Optimized_CNN-LSTM_with_Transfer_Learning/26808631CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/268086312023-09-13T09:00:00Z |
| spellingShingle | HIDM: Hybrid Intrusion Detection Model for Industry 4.0 Networks Using an Optimized CNN-LSTM with Transfer Learning Umesh Kumar Lilhore (17727684) Engineering Electrical engineering Information and computing sciences Artificial intelligence Cybersecurity and privacy Machine learning Industry 4.0 cyber security deep learning optimized CNN LSTM transfer learning GWO |
| status_str | publishedVersion |
| title | HIDM: Hybrid Intrusion Detection Model for Industry 4.0 Networks Using an Optimized CNN-LSTM with Transfer Learning |
| title_full | HIDM: Hybrid Intrusion Detection Model for Industry 4.0 Networks Using an Optimized CNN-LSTM with Transfer Learning |
| title_fullStr | HIDM: Hybrid Intrusion Detection Model for Industry 4.0 Networks Using an Optimized CNN-LSTM with Transfer Learning |
| title_full_unstemmed | HIDM: Hybrid Intrusion Detection Model for Industry 4.0 Networks Using an Optimized CNN-LSTM with Transfer Learning |
| title_short | HIDM: Hybrid Intrusion Detection Model for Industry 4.0 Networks Using an Optimized CNN-LSTM with Transfer Learning |
| title_sort | HIDM: Hybrid Intrusion Detection Model for Industry 4.0 Networks Using an Optimized CNN-LSTM with Transfer Learning |
| topic | Engineering Electrical engineering Information and computing sciences Artificial intelligence Cybersecurity and privacy Machine learning Industry 4.0 cyber security deep learning optimized CNN LSTM transfer learning GWO |