A novel intrusion detection framework for optimizing IoT security
<p dir="ltr">The emerging expanding scope of the Internet of Things (IoT) necessitates robust intrusion detection systems (IDS) to mitigate security risks effectively. However, existing approaches often struggle with adaptability to emerging threats and fail to account for IoT-specif...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , , , , |
| منشور في: |
2024
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1864513551176040448 |
|---|---|
| author | Abdul Qaddos (20748779) |
| author2 | Muhammad Usman Yaseen (9074552) Ahmad Sami Al-Shamayleh (17541495) Muhammad Imran (282621) Adnan Akhunzada (20151648) Salman Z. Alharthi (17541426) |
| author2_role | author author author author author |
| author_facet | Abdul Qaddos (20748779) Muhammad Usman Yaseen (9074552) Ahmad Sami Al-Shamayleh (17541495) Muhammad Imran (282621) Adnan Akhunzada (20151648) Salman Z. Alharthi (17541426) |
| author_role | author |
| dc.creator.none.fl_str_mv | Abdul Qaddos (20748779) Muhammad Usman Yaseen (9074552) Ahmad Sami Al-Shamayleh (17541495) Muhammad Imran (282621) Adnan Akhunzada (20151648) Salman Z. Alharthi (17541426) |
| dc.date.none.fl_str_mv | 2024-09-18T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1038/s41598-024-72049-z |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_novel_intrusion_detection_framework_for_optimizing_IoT_security/28441880 |
| 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 Artificial intelligence Cybersecurity and privacy Data management and data science Distributed computing and systems software Machine learning Internet of Things (IoT) Intrusion Detection System (IDS) Cybersecurity Convolutional Neural Network (CNN) Hybrid Model IoT Security Gated Recurrent Unit (GRU) |
| dc.title.none.fl_str_mv | A novel intrusion detection framework for optimizing IoT security |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">The emerging expanding scope of the Internet of Things (IoT) necessitates robust intrusion detection systems (IDS) to mitigate security risks effectively. However, existing approaches often struggle with adaptability to emerging threats and fail to account for IoT-specific complexities. To address these challenges, this study proposes a novel approach by hybridizing convolutional neural network (CNN) and gated recurrent unit (GRU) architectures tailored for IoT intrusion detection. This hybrid model excels in capturing intricate features and learning relational aspects crucial in IoT security. Moreover, we integrate the feature-weighted synthetic minority oversampling technique (FW-SMOTE) to handle imbalanced datasets, which commonly afflict intrusion detection tasks. Validation using the IoTID20 dataset, designed to emulate IoT environments, yields exceptional results with 99.60% accuracy in attack detection, surpassing existing benchmarks. Additionally, evaluation on the network domain dataset, UNSW-NB15, demonstrates robust performance with 99.16% accuracy, highlighting the model’s applicability across diverse datasets. This innovative approach not only addresses current limitations in IoT intrusion detection but also establishes new benchmarks in terms of accuracy and adaptability. The findings underscore its potential as a versatile and effective solution for safeguarding IoT ecosystems against evolving security threats.</p><h2>Other Information</h2><p dir="ltr">Published in: Scientific Reports<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/10.1038/s41598-024-72049-z" target="_blank">https://dx.doi.org/10.1038/s41598-024-72049-z</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_67d51ef33e0f1e2f95e4a4f7a75f82f0 |
| identifier_str_mv | 10.1038/s41598-024-72049-z |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/28441880 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A novel intrusion detection framework for optimizing IoT securityAbdul Qaddos (20748779)Muhammad Usman Yaseen (9074552)Ahmad Sami Al-Shamayleh (17541495)Muhammad Imran (282621)Adnan Akhunzada (20151648)Salman Z. Alharthi (17541426)Information and computing sciencesArtificial intelligenceCybersecurity and privacyData management and data scienceDistributed computing and systems softwareMachine learningInternet of Things (IoT)Intrusion Detection System (IDS)CybersecurityConvolutional Neural Network (CNN)Hybrid ModelIoT SecurityGated Recurrent Unit (GRU)<p dir="ltr">The emerging expanding scope of the Internet of Things (IoT) necessitates robust intrusion detection systems (IDS) to mitigate security risks effectively. However, existing approaches often struggle with adaptability to emerging threats and fail to account for IoT-specific complexities. To address these challenges, this study proposes a novel approach by hybridizing convolutional neural network (CNN) and gated recurrent unit (GRU) architectures tailored for IoT intrusion detection. This hybrid model excels in capturing intricate features and learning relational aspects crucial in IoT security. Moreover, we integrate the feature-weighted synthetic minority oversampling technique (FW-SMOTE) to handle imbalanced datasets, which commonly afflict intrusion detection tasks. Validation using the IoTID20 dataset, designed to emulate IoT environments, yields exceptional results with 99.60% accuracy in attack detection, surpassing existing benchmarks. Additionally, evaluation on the network domain dataset, UNSW-NB15, demonstrates robust performance with 99.16% accuracy, highlighting the model’s applicability across diverse datasets. This innovative approach not only addresses current limitations in IoT intrusion detection but also establishes new benchmarks in terms of accuracy and adaptability. The findings underscore its potential as a versatile and effective solution for safeguarding IoT ecosystems against evolving security threats.</p><h2>Other Information</h2><p dir="ltr">Published in: Scientific Reports<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/10.1038/s41598-024-72049-z" target="_blank">https://dx.doi.org/10.1038/s41598-024-72049-z</a></p>2024-09-18T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1038/s41598-024-72049-zhttps://figshare.com/articles/journal_contribution/A_novel_intrusion_detection_framework_for_optimizing_IoT_security/28441880CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/284418802024-09-18T09:00:00Z |
| spellingShingle | A novel intrusion detection framework for optimizing IoT security Abdul Qaddos (20748779) Information and computing sciences Artificial intelligence Cybersecurity and privacy Data management and data science Distributed computing and systems software Machine learning Internet of Things (IoT) Intrusion Detection System (IDS) Cybersecurity Convolutional Neural Network (CNN) Hybrid Model IoT Security Gated Recurrent Unit (GRU) |
| status_str | publishedVersion |
| title | A novel intrusion detection framework for optimizing IoT security |
| title_full | A novel intrusion detection framework for optimizing IoT security |
| title_fullStr | A novel intrusion detection framework for optimizing IoT security |
| title_full_unstemmed | A novel intrusion detection framework for optimizing IoT security |
| title_short | A novel intrusion detection framework for optimizing IoT security |
| title_sort | A novel intrusion detection framework for optimizing IoT security |
| topic | Information and computing sciences Artificial intelligence Cybersecurity and privacy Data management and data science Distributed computing and systems software Machine learning Internet of Things (IoT) Intrusion Detection System (IDS) Cybersecurity Convolutional Neural Network (CNN) Hybrid Model IoT Security Gated Recurrent Unit (GRU) |