Ensemble-Guard IoT: A Lightweight Ensemble Model for Real-Time Attack Detection on Imbalanced Dataset
<p dir="ltr">The rapid increase in the number of IoT devices has made ensuring robust real-time attack detection more critical than ever. The volume of data being accessed in real-time by these devices presents unique security challenges that traditional detection techniques struggle...
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2024
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| _version_ | 1864513539952082944 |
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| author | Muhammad Usama Tanveer (22225360) |
| author2 | Kashif Munir (6182237) Madiha Amjad (20036518) Syed Ali Jafar Zaidi (22225363) Amine Bermak (1895947) Atiq Ur Rehman (8843024) |
| author2_role | author author author author author |
| author_facet | Muhammad Usama Tanveer (22225360) Kashif Munir (6182237) Madiha Amjad (20036518) Syed Ali Jafar Zaidi (22225363) Amine Bermak (1895947) Atiq Ur Rehman (8843024) |
| author_role | author |
| dc.creator.none.fl_str_mv | Muhammad Usama Tanveer (22225360) Kashif Munir (6182237) Madiha Amjad (20036518) Syed Ali Jafar Zaidi (22225363) Amine Bermak (1895947) Atiq Ur Rehman (8843024) |
| dc.date.none.fl_str_mv | 2024-11-22T15:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2024.3495708 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Ensemble-Guard_IoT_A_Lightweight_Ensemble_Model_for_Real-Time_Attack_Detection_on_Imbalanced_Dataset/30095293 |
| 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 Machine learning Ensemble-guard IoT ensemble learning cybersecurity machine learning and real time attack detection Internet of Things Real-time systems Telecommunication traffic Object recognition Botnet Bayes methods Adaptation models |
| dc.title.none.fl_str_mv | Ensemble-Guard IoT: A Lightweight Ensemble Model for Real-Time Attack Detection on Imbalanced Dataset |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">The rapid increase in the number of IoT devices has made ensuring robust real-time attack detection more critical than ever. The volume of data being accessed in real-time by these devices presents unique security challenges that traditional detection techniques struggle to address with the required precision and efficiency. To overcome these limitations, we have developed Ensemble-Guard IoT; an innovative ensemble model combining Gaussian Naive Bayes (GNB), Logistic Regression (LR) and Random Forest (RF) through soft voting classifiers. Ensemble learning by combining multiple machine learning models offers a significant advantage in reducing computational costs compared to deep learning models, making it a practical solution for real-time applications. We performed a thorough evaluation of our proposed scheme in terms of accuracy 99.63%, precision1.00%, recall 99%, f1-score 1.00% and computation time 524.40s. We also compared the performance of our scheme with the classical schemes. Our comprehensive evaluation demonstrate that Ensemble-Guard achieves highest average accuracy of 99.63% thus validating the effectiveness of our scheme in identifying IoT attacks in real time. This hybrid voting system combines the predictions from different classifiers, ensuring a more balanced and accurate final decision. Ensemble-Guard IoT is a significant step forward in safeguarding IoT infrastructures, offering a scalable and cost-effective solution to the evolving threat landscape.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2024.3495708" target="_blank">https://dx.doi.org/10.1109/access.2024.3495708</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_7b38d97cb117459f4daf93986e046cc9 |
| identifier_str_mv | 10.1109/access.2024.3495708 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30095293 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Ensemble-Guard IoT: A Lightweight Ensemble Model for Real-Time Attack Detection on Imbalanced DatasetMuhammad Usama Tanveer (22225360)Kashif Munir (6182237)Madiha Amjad (20036518)Syed Ali Jafar Zaidi (22225363)Amine Bermak (1895947)Atiq Ur Rehman (8843024)Information and computing sciencesArtificial intelligenceCybersecurity and privacyData management and data scienceMachine learningEnsemble-guard IoTensemble learningcybersecuritymachine learning and real time attack detectionInternet of ThingsReal-time systemsTelecommunication trafficObject recognitionBotnetBayes methodsAdaptation models<p dir="ltr">The rapid increase in the number of IoT devices has made ensuring robust real-time attack detection more critical than ever. The volume of data being accessed in real-time by these devices presents unique security challenges that traditional detection techniques struggle to address with the required precision and efficiency. To overcome these limitations, we have developed Ensemble-Guard IoT; an innovative ensemble model combining Gaussian Naive Bayes (GNB), Logistic Regression (LR) and Random Forest (RF) through soft voting classifiers. Ensemble learning by combining multiple machine learning models offers a significant advantage in reducing computational costs compared to deep learning models, making it a practical solution for real-time applications. We performed a thorough evaluation of our proposed scheme in terms of accuracy 99.63%, precision1.00%, recall 99%, f1-score 1.00% and computation time 524.40s. We also compared the performance of our scheme with the classical schemes. Our comprehensive evaluation demonstrate that Ensemble-Guard achieves highest average accuracy of 99.63% thus validating the effectiveness of our scheme in identifying IoT attacks in real time. This hybrid voting system combines the predictions from different classifiers, ensuring a more balanced and accurate final decision. Ensemble-Guard IoT is a significant step forward in safeguarding IoT infrastructures, offering a scalable and cost-effective solution to the evolving threat landscape.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2024.3495708" target="_blank">https://dx.doi.org/10.1109/access.2024.3495708</a></p>2024-11-22T15:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2024.3495708https://figshare.com/articles/journal_contribution/Ensemble-Guard_IoT_A_Lightweight_Ensemble_Model_for_Real-Time_Attack_Detection_on_Imbalanced_Dataset/30095293CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/300952932024-11-22T15:00:00Z |
| spellingShingle | Ensemble-Guard IoT: A Lightweight Ensemble Model for Real-Time Attack Detection on Imbalanced Dataset Muhammad Usama Tanveer (22225360) Information and computing sciences Artificial intelligence Cybersecurity and privacy Data management and data science Machine learning Ensemble-guard IoT ensemble learning cybersecurity machine learning and real time attack detection Internet of Things Real-time systems Telecommunication traffic Object recognition Botnet Bayes methods Adaptation models |
| status_str | publishedVersion |
| title | Ensemble-Guard IoT: A Lightweight Ensemble Model for Real-Time Attack Detection on Imbalanced Dataset |
| title_full | Ensemble-Guard IoT: A Lightweight Ensemble Model for Real-Time Attack Detection on Imbalanced Dataset |
| title_fullStr | Ensemble-Guard IoT: A Lightweight Ensemble Model for Real-Time Attack Detection on Imbalanced Dataset |
| title_full_unstemmed | Ensemble-Guard IoT: A Lightweight Ensemble Model for Real-Time Attack Detection on Imbalanced Dataset |
| title_short | Ensemble-Guard IoT: A Lightweight Ensemble Model for Real-Time Attack Detection on Imbalanced Dataset |
| title_sort | Ensemble-Guard IoT: A Lightweight Ensemble Model for Real-Time Attack Detection on Imbalanced Dataset |
| topic | Information and computing sciences Artificial intelligence Cybersecurity and privacy Data management and data science Machine learning Ensemble-guard IoT ensemble learning cybersecurity machine learning and real time attack detection Internet of Things Real-time systems Telecommunication traffic Object recognition Botnet Bayes methods Adaptation models |