Bridging Innovation and Security: Advancing Cyber-Threat Detection in Sustainable Smart Infrastructure
<p dir="ltr">The rapid evolution of Smart Infrastructure (SI) on a global scale has revolutionized our daily lives, empowering us with unprecedented connectivity and convenience. However, this evolution has also exposed smart devices to increasingly sophisticated cyber-threats, endan...
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| مؤلفون آخرون: | , , , , |
| منشور في: |
2025
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إضافة وسم
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| _version_ | 1864513549652459520 |
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| author | Duaa Shoukat (20961200) |
| author2 | Adnan Akhunzada (20151648) Muhammad Taimoor Khan (12191713) Ahmad Sami Al-Shamayleh (17122985) Mueen Uddin (412034) Hashem Alaidaros (20961197) |
| author2_role | author author author author author |
| author_facet | Duaa Shoukat (20961200) Adnan Akhunzada (20151648) Muhammad Taimoor Khan (12191713) Ahmad Sami Al-Shamayleh (17122985) Mueen Uddin (412034) Hashem Alaidaros (20961197) |
| author_role | author |
| dc.creator.none.fl_str_mv | Duaa Shoukat (20961200) Adnan Akhunzada (20151648) Muhammad Taimoor Khan (12191713) Ahmad Sami Al-Shamayleh (17122985) Mueen Uddin (412034) Hashem Alaidaros (20961197) |
| dc.date.none.fl_str_mv | 2025-01-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1007/978-981-97-8588-9_11 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/chapter/Bridging_Innovation_and_Security_Advancing_Cyber-Threat_Detection_in_Sustainable_Smart_Infrastructure/28691948 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Cybersecurity and privacy Distributed computing and systems software Machine learning SI attacks SI challenges Multiclass detection DL models Threat detection |
| dc.title.none.fl_str_mv | Bridging Innovation and Security: Advancing Cyber-Threat Detection in Sustainable Smart Infrastructure |
| dc.type.none.fl_str_mv | Text Chapter info:eu-repo/semantics/publishedVersion text |
| description | <p dir="ltr">The rapid evolution of Smart Infrastructure (SI) on a global scale has revolutionized our daily lives, empowering us with unprecedented connectivity and convenience. However, this evolution has also exposed smart devices to increasingly sophisticated cyber-threats, endangering the integrity of entire smart networks. In response to these challenges, this paper proposes a novel approach utilizing Deep Learning (DL) models for multi-class threat detection in SI environments. Specifically, we introduce the Cu-GRULSTM model, which leverages CUDA-enabled Gated Recurrent Units (GRU) and Long Short-Term Memory (LSTM) architecture. Additionally, we employ the Cu-GRUDNN model for comparative analysis. Both models are trained and evaluated using the efficient and publicly available CICIDS2018 dataset. Our evaluation results demonstrate the superior performance of the proposed Cu-GRULSTM model, achieving an exceptional accuracy rate of 99.62% with a minimal False Alarms Rate (FAR) of 0.0003. This significant improvement over existing models underscores the efficacy of our approach in mitigating cyber-threats in smart infrastructure environments.</p><h2>Other Information</h2><p dir="ltr">Published in: Proceedings of the 1st International Conference on Creativity, Technology, and Sustainability<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See chapter on publisher's website: <a href="https://doi.org/10.1007/978-981-97-8588-9_11" target="_blank">https://doi.org/10.1007/978-981-97-8588-9_11</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_8da4b4075c7d744fb9a3fcf53aec4ace |
| identifier_str_mv | 10.1007/978-981-97-8588-9_11 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/28691948 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Bridging Innovation and Security: Advancing Cyber-Threat Detection in Sustainable Smart InfrastructureDuaa Shoukat (20961200)Adnan Akhunzada (20151648)Muhammad Taimoor Khan (12191713)Ahmad Sami Al-Shamayleh (17122985)Mueen Uddin (412034)Hashem Alaidaros (20961197)Information and computing sciencesCybersecurity and privacyDistributed computing and systems softwareMachine learningSI attacksSI challengesMulticlass detectionDL modelsThreat detection<p dir="ltr">The rapid evolution of Smart Infrastructure (SI) on a global scale has revolutionized our daily lives, empowering us with unprecedented connectivity and convenience. However, this evolution has also exposed smart devices to increasingly sophisticated cyber-threats, endangering the integrity of entire smart networks. In response to these challenges, this paper proposes a novel approach utilizing Deep Learning (DL) models for multi-class threat detection in SI environments. Specifically, we introduce the Cu-GRULSTM model, which leverages CUDA-enabled Gated Recurrent Units (GRU) and Long Short-Term Memory (LSTM) architecture. Additionally, we employ the Cu-GRUDNN model for comparative analysis. Both models are trained and evaluated using the efficient and publicly available CICIDS2018 dataset. Our evaluation results demonstrate the superior performance of the proposed Cu-GRULSTM model, achieving an exceptional accuracy rate of 99.62% with a minimal False Alarms Rate (FAR) of 0.0003. This significant improvement over existing models underscores the efficacy of our approach in mitigating cyber-threats in smart infrastructure environments.</p><h2>Other Information</h2><p dir="ltr">Published in: Proceedings of the 1st International Conference on Creativity, Technology, and Sustainability<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See chapter on publisher's website: <a href="https://doi.org/10.1007/978-981-97-8588-9_11" target="_blank">https://doi.org/10.1007/978-981-97-8588-9_11</a></p>2025-01-01T00:00:00ZTextChapterinfo:eu-repo/semantics/publishedVersiontext10.1007/978-981-97-8588-9_11https://figshare.com/articles/chapter/Bridging_Innovation_and_Security_Advancing_Cyber-Threat_Detection_in_Sustainable_Smart_Infrastructure/28691948CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/286919482025-01-01T00:00:00Z |
| spellingShingle | Bridging Innovation and Security: Advancing Cyber-Threat Detection in Sustainable Smart Infrastructure Duaa Shoukat (20961200) Information and computing sciences Cybersecurity and privacy Distributed computing and systems software Machine learning SI attacks SI challenges Multiclass detection DL models Threat detection |
| status_str | publishedVersion |
| title | Bridging Innovation and Security: Advancing Cyber-Threat Detection in Sustainable Smart Infrastructure |
| title_full | Bridging Innovation and Security: Advancing Cyber-Threat Detection in Sustainable Smart Infrastructure |
| title_fullStr | Bridging Innovation and Security: Advancing Cyber-Threat Detection in Sustainable Smart Infrastructure |
| title_full_unstemmed | Bridging Innovation and Security: Advancing Cyber-Threat Detection in Sustainable Smart Infrastructure |
| title_short | Bridging Innovation and Security: Advancing Cyber-Threat Detection in Sustainable Smart Infrastructure |
| title_sort | Bridging Innovation and Security: Advancing Cyber-Threat Detection in Sustainable Smart Infrastructure |
| topic | Information and computing sciences Cybersecurity and privacy Distributed computing and systems software Machine learning SI attacks SI challenges Multiclass detection DL models Threat detection |