A Survey on Feature Selection Techniques Based on Filtering Methods for Cyber Attack Detection

• Published in: Information (Basel) Vol. 14; no. 3; p. 191
• Main Authors: Lyu, Yang, Feng, Yaokai, Sakurai, Kouichi
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2023
• Subjects: Algorithms; Artificial intelligence; Classification; cyber attack detection; Cybersecurity; Cyberterrorism; Datasets; Detectors; Feature selection; filter-based feature selection techniques; Internet of Things; Methods; Search algorithms; Surveys; Germany
• ISSN: 2078-2489; 2078-2489
• DOI: 10.3390/info14030191

Cyber attack detection technology plays a vital role today, since cyber attacks have been causing great harm and loss to organizations and individuals. Feature selection is a necessary step for many cyber-attack detection systems, because it can reduce training costs, improve detection performance, and make the detection system lightweight. Many techniques related to feature selection for cyber attack detection have been proposed, and each technique has advantages and disadvantages. Determining which technology should be selected is a challenging problem for many researchers and system developers, and although there have been several survey papers on feature selection techniques in the field of cyber security, most of them try to be all-encompassing and are too general, making it difficult for readers to grasp the concrete and comprehensive image of the methods. In this paper, we survey the filter-based feature selection technique in detail and comprehensively for the first time. The filter-based technique is one popular kind of feature selection technique and is widely used in both research and application. In addition to general descriptions of this kind of method, we also explain in detail search algorithms and relevance measures, which are two necessary technical elements commonly used in the filter-based technique.