Hybrid deep learning based threat intelligence framework for Industrial IoT systems
<p>The exponential growth of Industrial Internet of Things (IIoT) is a major driving force behind Industry 4.0. Besides complete automation and transformation, industrial IoT has so far created plenty of opportunities in several sectors 1.3such as smart manufacturing, energy, healthcare, smart...
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| مؤلفون آخرون: | , , , |
| منشور في: |
2025
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| _version_ | 1864513521300013056 |
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| author | Jahanzaib Malik (23718816) |
| author2 | Adnan Akhunzada (20151648) Ahmad Sami Al-Shamayleh (17541495) Sherali Zeadally (13412781) Ahmad Almogren (13501312) |
| author2_role | author author author author |
| author_facet | Jahanzaib Malik (23718816) Adnan Akhunzada (20151648) Ahmad Sami Al-Shamayleh (17541495) Sherali Zeadally (13412781) Ahmad Almogren (13501312) |
| author_role | author |
| dc.creator.none.fl_str_mv | Jahanzaib Malik (23718816) Adnan Akhunzada (20151648) Ahmad Sami Al-Shamayleh (17541495) Sherali Zeadally (13412781) Ahmad Almogren (13501312) |
| dc.date.none.fl_str_mv | 2025-04-15T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.jii.2025.100846 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Hybrid_deep_learning_based_threat_intelligence_framework_for_Industrial_IoT_systems/31995108 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Engineering practice and education Information and computing sciences Cybersecurity and privacy Distributed computing and systems software Machine learning Artificial Intelligence (AI) Cyber Threat Intelligence (CTI) Industrial Internet of Things (IIoT) Network Security |
| dc.title.none.fl_str_mv | Hybrid deep learning based threat intelligence framework for Industrial IoT systems |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>The exponential growth of Industrial Internet of Things (IIoT) is a major driving force behind Industry 4.0. Besides complete automation and transformation, industrial IoT has so far created plenty of opportunities in several sectors 1.3such as smart manufacturing, energy, healthcare, smart agriculture, retail, supply chain, and transportation. However, the increased pervasiveness, reduced human involvement, resource-constrained nature of underlying IoT devices, dynamic and shared spectrum of 4G/5G communication, and reliance on the cloud for outsourced massive storage and computation bring novel security challenges and concerns. A significant challenge currently confronting the Industrial Internet of Things (IIoT) is the increasing prevalence of sophisticated IoT malware threats and attacks. To address this, the authors propose a hybrid threat intelligence framework that is not only highly scalable but also incorporates self-optimizing capabilities, enabling it to counteract a wide range of persistent cyber threats and attacks targeting IIoT systems. For a comprehensive evaluation, the authors utilized the state-of-the-art TON_IIoT dataset, which includes over 3 million instances representing various adversarial patterns and threat vectors. In addition, both standard and extended performance evaluation metrics were employed to ensure a thorough assessment. The proposed approach was also compared against several contemporary deep learning-based architectures and existing benchmark algorithms. The results indicate that the proposed method achieves superior detection accuracy, with only a minimal compromise in speed efficiency. Finally, a 10-fold cross-validation was conducted to provide an unbiased evaluation of the framework’s performance.</p><h2>Other Information</h2> <p> Published in: Journal of Industrial Information Integration<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.jii.2025.100846" target="_blank">https://dx.doi.org/10.1016/j.jii.2025.100846</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_599aa969502c97aaba8b0f8503ca6621 |
| identifier_str_mv | 10.1016/j.jii.2025.100846 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/31995108 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Hybrid deep learning based threat intelligence framework for Industrial IoT systemsJahanzaib Malik (23718816)Adnan Akhunzada (20151648)Ahmad Sami Al-Shamayleh (17541495)Sherali Zeadally (13412781)Ahmad Almogren (13501312)EngineeringEngineering practice and educationInformation and computing sciencesCybersecurity and privacyDistributed computing and systems softwareMachine learningArtificial Intelligence (AI)Cyber Threat Intelligence (CTI)Industrial Internet of Things (IIoT)Network Security<p>The exponential growth of Industrial Internet of Things (IIoT) is a major driving force behind Industry 4.0. Besides complete automation and transformation, industrial IoT has so far created plenty of opportunities in several sectors 1.3such as smart manufacturing, energy, healthcare, smart agriculture, retail, supply chain, and transportation. However, the increased pervasiveness, reduced human involvement, resource-constrained nature of underlying IoT devices, dynamic and shared spectrum of 4G/5G communication, and reliance on the cloud for outsourced massive storage and computation bring novel security challenges and concerns. A significant challenge currently confronting the Industrial Internet of Things (IIoT) is the increasing prevalence of sophisticated IoT malware threats and attacks. To address this, the authors propose a hybrid threat intelligence framework that is not only highly scalable but also incorporates self-optimizing capabilities, enabling it to counteract a wide range of persistent cyber threats and attacks targeting IIoT systems. For a comprehensive evaluation, the authors utilized the state-of-the-art TON_IIoT dataset, which includes over 3 million instances representing various adversarial patterns and threat vectors. In addition, both standard and extended performance evaluation metrics were employed to ensure a thorough assessment. The proposed approach was also compared against several contemporary deep learning-based architectures and existing benchmark algorithms. The results indicate that the proposed method achieves superior detection accuracy, with only a minimal compromise in speed efficiency. Finally, a 10-fold cross-validation was conducted to provide an unbiased evaluation of the framework’s performance.</p><h2>Other Information</h2> <p> Published in: Journal of Industrial Information Integration<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.jii.2025.100846" target="_blank">https://dx.doi.org/10.1016/j.jii.2025.100846</a></p>2025-04-15T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.jii.2025.100846https://figshare.com/articles/journal_contribution/Hybrid_deep_learning_based_threat_intelligence_framework_for_Industrial_IoT_systems/31995108CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/319951082025-04-15T03:00:00Z |
| spellingShingle | Hybrid deep learning based threat intelligence framework for Industrial IoT systems Jahanzaib Malik (23718816) Engineering Engineering practice and education Information and computing sciences Cybersecurity and privacy Distributed computing and systems software Machine learning Artificial Intelligence (AI) Cyber Threat Intelligence (CTI) Industrial Internet of Things (IIoT) Network Security |
| status_str | publishedVersion |
| title | Hybrid deep learning based threat intelligence framework for Industrial IoT systems |
| title_full | Hybrid deep learning based threat intelligence framework for Industrial IoT systems |
| title_fullStr | Hybrid deep learning based threat intelligence framework for Industrial IoT systems |
| title_full_unstemmed | Hybrid deep learning based threat intelligence framework for Industrial IoT systems |
| title_short | Hybrid deep learning based threat intelligence framework for Industrial IoT systems |
| title_sort | Hybrid deep learning based threat intelligence framework for Industrial IoT systems |
| topic | Engineering Engineering practice and education Information and computing sciences Cybersecurity and privacy Distributed computing and systems software Machine learning Artificial Intelligence (AI) Cyber Threat Intelligence (CTI) Industrial Internet of Things (IIoT) Network Security |