Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/61380
Title: A fuzzy-weighted approach for malicious web domain identification
Contributor(s): Wang, Zuli (author); Chiong, Raymond  (author)orcid ; Fan, Zongwen (author)
Publication Date: 2021
DOI: 10.3233/JIFS-200943
Handle Link: https://hdl.handle.net/1959.11/61380
Abstract: 

Malicious web domains represent a serious threat to online users' privacy and security, causing monetary loss, theft of private information, and malware attacks, among others. In recent years, machine learning methods have been widely used as prediction models to identify malicious web domains. In this study, we propose a Fuzzy-Weighted Least Squares Support Vector Machine (FW-LS-SVM) model for malicious web domain identification. In our proposed model, a fuzzyweighted operation is applied to each data sample considering the fact that different samples may have different importance. This fuzzy-weighted operation is also able to alleviate the influence of noise data and improve the model's robustness by assigning weights to error constraints. For comparison purposes, three commonly used single machine learning classifiers and three widely used ensemble models are included in our experiments, in order to assess the performance of our proposed FW-LS-SVM and its ensemble version. Hyperlink indicators and uniform resource locator-based features are used to train the prediction models. Experimental results show that our proposed approach is highly effective in identifying malicious web domains, outperforming the well-established single and ensemble models being compared.

Publication Type: Journal Article
Source of Publication: Journal of Intelligent and Fuzzy Systems, 41(2), p. 2551-2559
Publisher: IOS Press
Place of Publication: The Netherlands
ISSN: 1875-8967
1064-1246
Fields of Research (FoR) 2020: 4602 Artificial intelligence
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Journal Article
School of Science and Technology

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