Archimedes Fire Hawk Optimization enabled feature selection with deep maxout for network intrusion detection
Network intrusion detection systems (NIDSs) are fundamental for protecting computer networks. In this paper, the Archimedes Fire Hawk Optimization (AFHO) enabled Deep Learning (DL) is developed. At first, the Wireless Network is initialized, and then the recorded log file is passed through the pre-p...
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| Vydáno v: | Computers & security Ročník 140; s. 103751 |
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| Jazyk: | angličtina |
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Elsevier Ltd
01.05.2024
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| ISSN: | 0167-4048, 1872-6208 |
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| Abstract | Network intrusion detection systems (NIDSs) are fundamental for protecting computer networks. In this paper, the Archimedes Fire Hawk Optimization (AFHO) enabled Deep Learning (DL) is developed. At first, the Wireless Network is initialized, and then the recorded log file is passed through the pre-processing phase utilizing quantile normalization, and missing data imputation process. Next to this, the feature selection is done with the help of AFHO, where AFHO is the combination of Archimedes optimization algorithm (AOA), and Fire Hawk optimization (FHO). At last, the intrusion detection (ID) is accomplished by Deep Maxout Network (DMN), which is tuned employing the proposed AFHO. The performance measures such as precision, recall and F-measure based on training data, where the K- fold acquired 93.7 %, 97.7 %, and 95.6 %. |
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| AbstractList | Network intrusion detection systems (NIDSs) are fundamental for protecting computer networks. In this paper, the Archimedes Fire Hawk Optimization (AFHO) enabled Deep Learning (DL) is developed. At first, the Wireless Network is initialized, and then the recorded log file is passed through the pre-processing phase utilizing quantile normalization, and missing data imputation process. Next to this, the feature selection is done with the help of AFHO, where AFHO is the combination of Archimedes optimization algorithm (AOA), and Fire Hawk optimization (FHO). At last, the intrusion detection (ID) is accomplished by Deep Maxout Network (DMN), which is tuned employing the proposed AFHO. The performance measures such as precision, recall and F-measure based on training data, where the K- fold acquired 93.7 %, 97.7 %, and 95.6 %. |
| ArticleNumber | 103751 |
| Author | Vairamuthu, S Rani, B. Selva Subramanian, Suresh |
| Author_xml | – sequence: 1 givenname: B. Selva surname: Rani fullname: Rani, B. Selva email: bselvarani@vit.ac.in organization: Department of Information Technology, School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore. Tamilnadu, 632014, India – sequence: 2 givenname: S surname: Vairamuthu fullname: Vairamuthu, S email: svairamuthu@vit.ac.in organization: Department of Software Systems, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore. Tamilnadu, 632014, India – sequence: 3 givenname: Suresh surname: Subramanian fullname: Subramanian, Suresh email: ssubramanian@ahlia.edu.bh organization: Chairperson of Multimedia Science Department, College of Information Technology, Ahlia University, Kingdom of Bahrain |
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| Cites_doi | 10.1155/2020/8833767 10.1109/ACCESS.2019.2899721 10.1109/ACCESS.2019.2895334 10.1109/TETCI.2017.2772792 10.1109/ACCESS.2020.2973730 10.1016/j.comcom.2015.02.004 10.1007/s10489-020-01893-z 10.1016/j.neucom.2017.05.103 10.1109/COMST.2014.2336610 10.46253/j.mr.v4i4.a5 10.1016/j.iotcps.2021.08.001 10.1016/j.eswa.2009.05.029 10.1155/2020/8867792 10.1007/s10462-022-10173-w 10.1109/ACCESS.2019.2917299 10.1109/ACCESS.2020.2972627 10.1109/ACCESS.2018.2841987 10.1016/j.comcom.2014.01.012 10.1109/ACCESS.2019.2905041 10.1109/ACCESS.2019.2905633 |
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| Keywords | Wireless network Deep Maxout network (DMN) Archimedes optimization algorithm (AOA) Fire Hawk optimization (FHO) Network intrusion detection |
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| Title | Archimedes Fire Hawk Optimization enabled feature selection with deep maxout for network intrusion detection |
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