Cyber Intrusion Detection System Based on a Multiobjective Binary Bat Algorithm for Feature Selection and Enhanced Bat Algorithm for Parameter Optimization in Neural Networks
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 th...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9832899/ |
| _version_ | 1828190365202513920 |
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| author | Waheed Ali H. M. Ghanem Sanaa Abduljabbar Ahmed Ghaleb Aman Jantan Abdullah B. Nasser Sami Abdulla Mohsen Saleh Amir Ngah Arifah Che Alhadi Humaira Arshad Abdul-Malik H. Y. Saad Abiodun Esther Omolara Yousef A. Baker El-Ebiary Oludare Isaac Abiodun |
| author_facet | Waheed Ali H. M. Ghanem Sanaa Abduljabbar Ahmed Ghaleb Aman Jantan Abdullah B. Nasser Sami Abdulla Mohsen Saleh Amir Ngah Arifah Che Alhadi Humaira Arshad Abdul-Malik H. Y. Saad Abiodun Esther Omolara Yousef A. Baker El-Ebiary Oludare Isaac Abiodun |
| author_sort | Waheed Ali H. M. Ghanem |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T08:19:56Z |
| format | Article |
| id | doaj.art-8b0c18c2e28c4e3da478775a8e1bed6b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T08:19:56Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-8b0c18c2e28c4e3da478775a8e1bed6b2022-12-22T03:40:36ZengIEEEIEEE Access2169-35362022-01-0110763187633910.1109/ACCESS.2022.31924729832899Cyber Intrusion Detection System Based on a Multiobjective Binary Bat Algorithm for Feature Selection and Enhanced Bat Algorithm for Parameter Optimization in Neural NetworksWaheed Ali H. M. Ghanem0https://orcid.org/0000-0002-3764-4788Sanaa Abduljabbar Ahmed Ghaleb1https://orcid.org/0000-0003-4506-5214Aman Jantan2Abdullah B. Nasser3Sami Abdulla Mohsen Saleh4Amir Ngah5Arifah Che Alhadi6Humaira Arshad7Abdul-Malik H. Y. Saad8Abiodun Esther Omolara9Yousef A. Baker El-Ebiary10Oludare Isaac Abiodun11Faculty of Engineering, University of Aden, Aden, YemenFaculty of Engineering, University of Aden, Aden, YemenSchool of Computer Science, Universiti Sains Malaysia, Pinang, MalaysiaSchool of Technology and Innovation, University of Vaasa, Vaasa, FinlandSchool of Electrical and Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal, Pulau Pinang, MalaysiaFaculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, Terengganu, MalaysiaFaculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, Terengganu, MalaysiaDepartment of Computer Science, Islamia University of Bahawalpur, Bahawalpur, PakistanDivision of Electronic and Computer Engineering, School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Johor, MalaysiaDepartment of Computer Science, University of Abuja, Gwagwalada, NigeriaFaculty of Informatics and Computing, Universiti Sultan Zainal Abidin, Terengganu, MalaysiaDepartment of Computer Science, Bingham University, Karu, NigeriaThe 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.https://ieeexplore.ieee.org/document/9832899/Intrusion detection system (IDS)bat algorithm (BAT)metaheuristic algorithm (MA)feature selection (FS)multi-objective optimization (MOO)multilayer perceptron (MLP) |
| spellingShingle | Waheed Ali H. M. Ghanem Sanaa Abduljabbar Ahmed Ghaleb Aman Jantan Abdullah B. Nasser Sami Abdulla Mohsen Saleh Amir Ngah Arifah Che Alhadi Humaira Arshad Abdul-Malik H. Y. Saad Abiodun Esther Omolara Yousef A. Baker El-Ebiary Oludare Isaac Abiodun Cyber Intrusion Detection System Based on a Multiobjective Binary Bat Algorithm for Feature Selection and Enhanced Bat Algorithm for Parameter Optimization in Neural Networks IEEE Access Intrusion detection system (IDS) bat algorithm (BAT) metaheuristic algorithm (MA) feature selection (FS) multi-objective optimization (MOO) multilayer perceptron (MLP) |
| title | Cyber Intrusion Detection System Based on a Multiobjective Binary Bat Algorithm for Feature Selection and Enhanced Bat Algorithm for Parameter Optimization in Neural Networks |
| title_full | Cyber Intrusion Detection System Based on a Multiobjective Binary Bat Algorithm for Feature Selection and Enhanced Bat Algorithm for Parameter Optimization in Neural Networks |
| title_fullStr | Cyber Intrusion Detection System Based on a Multiobjective Binary Bat Algorithm for Feature Selection and Enhanced Bat Algorithm for Parameter Optimization in Neural Networks |
| title_full_unstemmed | Cyber Intrusion Detection System Based on a Multiobjective Binary Bat Algorithm for Feature Selection and Enhanced Bat Algorithm for Parameter Optimization in Neural Networks |
| title_short | Cyber Intrusion Detection System Based on a Multiobjective Binary Bat Algorithm for Feature Selection and Enhanced Bat Algorithm for Parameter Optimization in Neural Networks |
| title_sort | cyber intrusion detection system based on a multiobjective binary bat algorithm for feature selection and enhanced bat algorithm for parameter optimization in neural networks |
| topic | Intrusion detection system (IDS) bat algorithm (BAT) metaheuristic algorithm (MA) feature selection (FS) multi-objective optimization (MOO) multilayer perceptron (MLP) |
| url | https://ieeexplore.ieee.org/document/9832899/ |
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