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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Bibliographic Details
Published in:	Computers & security Vol. 140; p. 103751
Main Authors:	Rani, B. Selva, Vairamuthu, S, Subramanian, Suresh
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.05.2024
Subjects:	Archimedes optimization algorithm (AOA) Deep Maxout network (DMN) Fire Hawk optimization (FHO) Network intrusion detection Wireless network Wireless network Deep Maxout network (DMN) Archimedes optimization algorithm (AOA) Fire Hawk optimization (FHO) Network intrusion detection
ISSN:	0167-4048, 1872-6208
Online Access:	Get full text
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Summary:	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 %.
ISSN:	0167-4048 1872-6208
DOI:	10.1016/j.cose.2024.103751