Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/61466
Title: Identifying malicious web domains using machine learning techniques with online credibility and performance data
Contributor(s): Hu, Zhongyi (author); Chiong, Raymond  (author)orcid ; Pranata, Ilung (author); Susilo, Willy (author); Bao, Yukun (author)
Publication Date: 2016
DOI: 10.1109/CEC.2016.7748347
Handle Link: https://hdl.handle.net/1959.11/61466
Abstract: 

Malicious web domains represent a big threat to web users' privacy and security. With so much freely available data on the Internet about web domains' popularity and performance, this study investigated the performance of well-known machine learning techniques used in conjunction with this type of online data to identify malicious web domains. Two datasets consisting of malware and phishing domains were collected to build and evaluate the machine learning classifiers. Five single classifiers and four ensemble classifiers were applied to distinguish malicious domains from benign ones. In addition, a binary particle swarm optimisation (BPSO) based feature selection method was used to improve the performance of single classifiers. Experimental results show that, based on the web domains' popularity and performance data features, the examined machine learning techniques can accurately identify malicious domains in different ways. Furthermore, the BPSO-based feature selection procedure is shown to be an effective way to improve the performance of classifiers.

Publication Type: Conference Publication
Conference Details: 2016 IEEE CEC: Congress on Evolutionary Computation, Vancouver, Canada, 24th - 29th July, 2016
Source of Publication: Proceedings of the 2016 IEEE Congress on Evolutionary Computation, p. 5186-5194
Publisher: IEEE
Place of Publication: United States of America
Fields of Research (FoR) 2020: 4602 Artificial intelligence
Peer Reviewed: Yes
HERDC Category Description: E1 Refereed Scholarly Conference Publication
Appears in Collections:Conference Publication
School of Science and Technology

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