Identifying malicious web domains using machine learning techniques with online credibility and performance data

Author(s)
Hu, Zhongyi
Chiong, Raymond
Pranata, Ilung
Susilo, Willy
Bao, Yukun
Publication Date
2016
Abstract
<p>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.</p>
Citation
Proceedings of the 2016 IEEE Congress on Evolutionary Computation, p. 5186-5194
ISBN
9781509006236
9781509006229
Link
Publisher
IEEE
Title
Identifying malicious web domains using machine learning techniques with online credibility and performance data
Type of document
Conference Publication
Entity Type
Publication

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