Feature optimization and hybrid classification for malicious web page detection. (6th July 2020)

• Record Type: Journal Article
• Authors: Deng, Weiping; Peng, Yan; Yang, Fan; Song, Jun
• Is Part Of: Concurrency and computation. Volume 34:Number 16(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-07-06
• Subjects: classifier fusion -- feature selection -- information gain -- machine learning -- malicious web page; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5859
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: The security threats from malicious web pages have become a hot topic for cyber security. One goal pursued by current research is to identify malicious web pages quickly, accurately, and efficiently. Considering the high detection costs and potential dimensionality curse of malicious webpage detection, in this article, we proposes a detection framework based on feature optimization and hybrid classification. It provides three properties: more new malicious webpage features, information gain‐based feature selection method, and integrating multiple machine learning method. A comprehensive experimental evaluation demonstrates that the proposed framework has remarkable advantages in aspects of detection accuracy and detection performance.