Modified Aquila Optimizer Feature Selection Approach and Support Vector Machine Classifier for Intrusion Detection System
With the ever-expanding ubiquity of the Internet, wireless networks have permeated every facet of modern life, escalating concerns surrounding network security for users. Consequently, the demand for a robust Intrusion Detection System (IDS) has surged. The IDS serves as a critical bastion within th...
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| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , , , , , , |
| منشور في: |
2024
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://depot.sorbonne.ae/handle/20.500.12458/1456 |
| الوسوم: |
إضافة وسم
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| _version_ | 1857415064432148480 |
|---|---|
| author | Abualigah, Laith |
| author2 | Ahmed, Saba Hussein Almomani, Mohammad H. Abu Zitar, Raed Alsoud, Anas Ratib Abuhaija, Belal Hanandeh, Essam Said Jia, Heming Abd Elminaam, Diaa Salama Abd Elaziz, Mohamed |
| author2_role | author author author author author author author author author |
| author_facet | Abualigah, Laith Ahmed, Saba Hussein Almomani, Mohammad H. Abu Zitar, Raed Alsoud, Anas Ratib Abuhaija, Belal Hanandeh, Essam Said Jia, Heming Abd Elminaam, Diaa Salama Abd Elaziz, Mohamed |
| author_role | author |
| dc.creator.none.fl_str_mv | Abualigah, Laith Ahmed, Saba Hussein Almomani, Mohammad H. Abu Zitar, Raed Alsoud, Anas Ratib Abuhaija, Belal Hanandeh, Essam Said Jia, Heming Abd Elminaam, Diaa Salama Abd Elaziz, Mohamed |
| dc.date.none.fl_str_mv | 2024-01-03T06:05:39Z 2024-01-03T06:05:39Z 2024 |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | 1573-7721 https://depot.sorbonne.ae/handle/20.500.12458/1456 10.1007/s11042-023-17886-2 |
| dc.language.none.fl_str_mv | en |
| dc.relation.none.fl_str_mv | Multimedia Tools and Applications |
| dc.subject.none.fl_str_mv | Aquila optimizer Intrusion detection system Support vector machine classifier Feature selection |
| dc.title.none.fl_str_mv | Modified Aquila Optimizer Feature Selection Approach and Support Vector Machine Classifier for Intrusion Detection System |
| dc.type.none.fl_str_mv | Controlled Vocabulary for Resource Type Genres::text::periodical::journal::contribution to journal::journal article |
| description | With the ever-expanding ubiquity of the Internet, wireless networks have permeated every facet of modern life, escalating concerns surrounding network security for users. Consequently, the demand for a robust Intrusion Detection System (IDS) has surged. The IDS serves as a critical bastion within the security framework, a significance further magnified in wireless networks where intrusions may stem from the deluge of sensor data. This influx of data, however, inevitably taxes the efficiency and computational speed of IDS. To address these limitations, numerous strategies for enhancing IDS performance have been posited by researchers. This paper introduces a novel feature selection method grounded in Support Vector Machine (SVM) and harnessing the innovative modified Aquila Optimizer (mAO) for Intrusion Detection Systems in Wireless Sensor Networks. To evaluate the efficacy of our approach, we employed the KDD'99 dataset for testing and benchmarking against established methods. Multiple performance metrics, including accuracy, detection rate, false alarm rate, feature count, and execution time, were utilized for assessment. Our comparative analysis reveals the superiority of the proposed method, with standout results in terms of feature reduction, detection accuracy, and false alarm mitigation, yielding significant improvements of 11%, 98.76%, and 0.02%, respectively. |
| id | sorbonner_58b76e560419dfd30129bf89e9bcb9b8 |
| identifier_str_mv | 1573-7721 10.1007/s11042-023-17886-2 |
| language_invalid_str_mv | en |
| network_acronym_str | sorbonner |
| network_name_str | Sorbonne University Abu Dhabi repository |
| oai_identifier_str | oai:depot.sorbonne.ae:20.500.12458/1456 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Modified Aquila Optimizer Feature Selection Approach and Support Vector Machine Classifier for Intrusion Detection SystemAbualigah, LaithAhmed, Saba HusseinAlmomani, Mohammad H.Abu Zitar, RaedAlsoud, Anas RatibAbuhaija, BelalHanandeh, Essam SaidJia, HemingAbd Elminaam, Diaa SalamaAbd Elaziz, MohamedAquila optimizerIntrusion detection systemSupport vector machine classifierFeature selectionWith the ever-expanding ubiquity of the Internet, wireless networks have permeated every facet of modern life, escalating concerns surrounding network security for users. Consequently, the demand for a robust Intrusion Detection System (IDS) has surged. The IDS serves as a critical bastion within the security framework, a significance further magnified in wireless networks where intrusions may stem from the deluge of sensor data. This influx of data, however, inevitably taxes the efficiency and computational speed of IDS. To address these limitations, numerous strategies for enhancing IDS performance have been posited by researchers. This paper introduces a novel feature selection method grounded in Support Vector Machine (SVM) and harnessing the innovative modified Aquila Optimizer (mAO) for Intrusion Detection Systems in Wireless Sensor Networks. To evaluate the efficacy of our approach, we employed the KDD'99 dataset for testing and benchmarking against established methods. Multiple performance metrics, including accuracy, detection rate, false alarm rate, feature count, and execution time, were utilized for assessment. Our comparative analysis reveals the superiority of the proposed method, with standout results in terms of feature reduction, detection accuracy, and false alarm mitigation, yielding significant improvements of 11%, 98.76%, and 0.02%, respectively.2024-01-03T06:05:39Z2024-01-03T06:05:39Z2024Controlled Vocabulary for Resource Type Genres::text::periodical::journal::contribution to journal::journal articleapplication/pdf1573-7721https://depot.sorbonne.ae/handle/20.500.12458/145610.1007/s11042-023-17886-2enMultimedia Tools and Applicationsoai:depot.sorbonne.ae:20.500.12458/14562024-07-17T18:00:34Z |
| spellingShingle | Modified Aquila Optimizer Feature Selection Approach and Support Vector Machine Classifier for Intrusion Detection System Abualigah, Laith Aquila optimizer Intrusion detection system Support vector machine classifier Feature selection |
| title | Modified Aquila Optimizer Feature Selection Approach and Support Vector Machine Classifier for Intrusion Detection System |
| title_full | Modified Aquila Optimizer Feature Selection Approach and Support Vector Machine Classifier for Intrusion Detection System |
| title_fullStr | Modified Aquila Optimizer Feature Selection Approach and Support Vector Machine Classifier for Intrusion Detection System |
| title_full_unstemmed | Modified Aquila Optimizer Feature Selection Approach and Support Vector Machine Classifier for Intrusion Detection System |
| title_short | Modified Aquila Optimizer Feature Selection Approach and Support Vector Machine Classifier for Intrusion Detection System |
| title_sort | Modified Aquila Optimizer Feature Selection Approach and Support Vector Machine Classifier for Intrusion Detection System |
| topic | Aquila optimizer Intrusion detection system Support vector machine classifier Feature selection |
| url | https://depot.sorbonne.ae/handle/20.500.12458/1456 |