دورية أكاديمية
Cyber Intrusion Detection System Based on a Multiobjective Binary Bat Algorithm for Feature Selection and Enhanced Bat Algorithm for Parameter Optimization in Neural Networks
| العنوان: | Cyber Intrusion Detection System Based on a Multiobjective Binary Bat Algorithm for Feature Selection and Enhanced Bat Algorithm for Parameter Optimization in Neural Networks |
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| المؤلفون: | Ghanem, Waheed Ali H. M., Ghaleb, Sanaa Abduljabbar Ahmed, Jantan, Aman, Nasser, Abdullah B., Saleh, Sami Abdulla Mohsen, Ngah, Amir, Alhadi, Arifah Che, Arshad, Humaira, Saad, Abdul-Malik H. Y., Omolara, Abiodun Esther, El-Ebiary, Yousef A. Baker, Abiodun, Oludare Isaac |
| المساهمون: | fi=Vaasan yliopisto|en=University of Vaasa, orcid:0000-0002-5377-999X, fi=Tekniikan ja innovaatiojohtamisen yksikkö|en=School of Technology and Innovations, Innolab |
| المصدر: | WOS:000831066600001 ; Scopus:85135221673 |
| بيانات النشر: | IEEE |
| سنة النشر: | 2022 |
| المجموعة: | Osuva (University of Vaasa) |
| مصطلحات موضوعية: | bat algorithm (BAT), feature selection (FS), Intrusion detection system (IDS), metaheuristic algorithm (MA), multi-objective optimization (MOO), multilayer perceptron (MLP), fi=Tietotekniikka|en=Computer Science |
| الوصف: | The staggering development of cyber threats has propelled experts, professionals and specialists in the field of security into the development of more dependable protection systems, including effective intrusion detection system (IDS) mechanisms which are equipped for boosting accurately detected threats and limiting erroneously detected threats simultaneously. Nonetheless, the proficiency of the IDS framework depends essentially on extracted features from network traffic and an effective classifier of the traffic into abnormal or normal traffic. The prime impetus of this study is to increase the performance of the IDS on networks by building a two-phase framework to reinforce and subsequently enhance detection rate and diminish the rate of false alarm. The initial stage utilizes the developed algorithm of a proficient wrapper-approach-based feature selection which is created on a multi-objective BAT algorithm (MOBBAT). The subsequent stage utilizes the features obtained from the initial stage to categorize the traffic based on the newly upgraded BAT algorithm (EBAT) for training multilayer perceptron (EBATMLP), to improve the IDS performance. The resulting methodology is known as the (MOB-EBATMLP). The efficiency of our proposition has been assessed by utilizing the mainstream benchmarked datasets: NLS-KDD, ISCX2012, UNSW-NB15, KDD CUP 1999, and CICIDS2017 which are established as standard datasets for evaluating IDS. The outcome of our experimental analysis demonstrates a noteworthy advancement in network IDS above other techniques. ; ©2022 the Authors. Published by IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0Test/ ; fi=vertaisarvioitu|en=peerReviewed |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | fi=kokoteksti|en=fulltext; 76318-76339 |
| اللغة: | English |
| تدمد: | 2169-3536 |
| العلاقة: | IEEE Access; 10; https://doi.org/10.1109/ACCESS.2022.3192472Test; https://osuva.uwasa.fi/handle/10024/14919Test; URN:NBN:fi-fe2022122873953 |
| الإتاحة: | https://doi.org/10.1109/ACCESS.2022.3192472Test https://osuva.uwasa.fi/handle/10024/14919Test |
| حقوق: | CC BY 4.0 |
| رقم الانضمام: | edsbas.1D836144 |
| قاعدة البيانات: | BASE |
Full Text Finder | تدمد: | 21693536 |
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