Feature Subset Selection Hybrid Deep Belief Network Based Cybersecurity Intrusion Detection Model
Intrusion detection system (IDS) has played a significant role in modern network security. A key component for constructing an effective IDS is the identification of essential features and network traffic data preprocessing to design effective classification model. This paper presents a Feature Subs...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/11/19/3077 |
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| author | Khalid A. Alissa Hadil Shaiba Abdulbaset Gaddah Ayman Yafoz Raed Alsini Omar Alghushairy Amira Sayed A. Aziz Mesfer Al Duhayyim |
| author_facet | Khalid A. Alissa Hadil Shaiba Abdulbaset Gaddah Ayman Yafoz Raed Alsini Omar Alghushairy Amira Sayed A. Aziz Mesfer Al Duhayyim |
| author_sort | Khalid A. Alissa |
| collection | DOAJ |
| description | Intrusion detection system (IDS) has played a significant role in modern network security. A key component for constructing an effective IDS is the identification of essential features and network traffic data preprocessing to design effective classification model. This paper presents a Feature Subset Selection Hybrid Deep Belief Network based Cybersecurity Intrusion Detection (FSHDBN-CID) model. The presented FSHDBN-CID model mainly concentrates on the recognition of intrusions to accomplish cybersecurity in the network. In the presented FSHDBN-CID model, different levels of data preprocessing can be performed to transform the raw data into compatible format. For feature selection purposes, jaya optimization algorithm (JOA) is utilized which in turn reduces the computation complexity. In addition, the presented FSHDBN-CID model exploits HDBN model for classification purposes. At last, chicken swarm optimization (CSO) technique can be implemented as a hyperparameter optimizer for the HDBN method. In order to investigate the enhanced performance of the presented FSHDBN-CID method, a wide range of experiments was performed. The comparative study pointed out the improvements of the FSHDBN-CID model over other models with an accuracy of 99.57%. |
| first_indexed | 2024-03-09T21:52:16Z |
| format | Article |
| id | doaj.art-dd603f54b0da4e05a66eae3671a51bb3 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T21:52:16Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-dd603f54b0da4e05a66eae3671a51bb32023-11-23T20:05:54ZengMDPI AGElectronics2079-92922022-09-011119307710.3390/electronics11193077Feature Subset Selection Hybrid Deep Belief Network Based Cybersecurity Intrusion Detection ModelKhalid A. Alissa0Hadil Shaiba1Abdulbaset Gaddah2Ayman Yafoz3Raed Alsini4Omar Alghushairy5Amira Sayed A. Aziz6Mesfer Al Duhayyim7Saudi Aramco Cybersecurity Chair, Networks and Communications Department, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi ArabiaDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Computer Sciences, College of Computing and Information System, Umm Al-Qura University, Mecca 24382, Saudi ArabiaDepartment of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 22254, Saudi ArabiaDepartment of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 22254, Saudi ArabiaDepartment of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah 21589, Saudi ArabiaDepartment of Digital Media, Faculty of Computers and Information Technology, Future University in Egypt, New Cairo 11835, EgyptDepartment of Computer Science, College of Sciences and Humanities—Aflaj, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi ArabiaIntrusion detection system (IDS) has played a significant role in modern network security. A key component for constructing an effective IDS is the identification of essential features and network traffic data preprocessing to design effective classification model. This paper presents a Feature Subset Selection Hybrid Deep Belief Network based Cybersecurity Intrusion Detection (FSHDBN-CID) model. The presented FSHDBN-CID model mainly concentrates on the recognition of intrusions to accomplish cybersecurity in the network. In the presented FSHDBN-CID model, different levels of data preprocessing can be performed to transform the raw data into compatible format. For feature selection purposes, jaya optimization algorithm (JOA) is utilized which in turn reduces the computation complexity. In addition, the presented FSHDBN-CID model exploits HDBN model for classification purposes. At last, chicken swarm optimization (CSO) technique can be implemented as a hyperparameter optimizer for the HDBN method. In order to investigate the enhanced performance of the presented FSHDBN-CID method, a wide range of experiments was performed. The comparative study pointed out the improvements of the FSHDBN-CID model over other models with an accuracy of 99.57%.https://www.mdpi.com/2079-9292/11/19/3077intrusion detection systemdeep belief networkfeature selectionnetwork securitychicken swarm optimization |
| spellingShingle | Khalid A. Alissa Hadil Shaiba Abdulbaset Gaddah Ayman Yafoz Raed Alsini Omar Alghushairy Amira Sayed A. Aziz Mesfer Al Duhayyim Feature Subset Selection Hybrid Deep Belief Network Based Cybersecurity Intrusion Detection Model Electronics intrusion detection system deep belief network feature selection network security chicken swarm optimization |
| title | Feature Subset Selection Hybrid Deep Belief Network Based Cybersecurity Intrusion Detection Model |
| title_full | Feature Subset Selection Hybrid Deep Belief Network Based Cybersecurity Intrusion Detection Model |
| title_fullStr | Feature Subset Selection Hybrid Deep Belief Network Based Cybersecurity Intrusion Detection Model |
| title_full_unstemmed | Feature Subset Selection Hybrid Deep Belief Network Based Cybersecurity Intrusion Detection Model |
| title_short | Feature Subset Selection Hybrid Deep Belief Network Based Cybersecurity Intrusion Detection Model |
| title_sort | feature subset selection hybrid deep belief network based cybersecurity intrusion detection model |
| topic | intrusion detection system deep belief network feature selection network security chicken swarm optimization |
| url | https://www.mdpi.com/2079-9292/11/19/3077 |
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