- Title
- Identifying malicious web domains using machine learning techniques with online credibility and performance data
- Creator
- Hu, Zhongyi; Chiong, Raymond; Pranata, Ilung; Susilo, Willy; Bao, Yukun
- Relation
- 2016 IEEE Congress on Evolutionary Computation (CEC). Proceedings of the 2016 IEEE Congress on Evolutionary Computation (CEC) (Vancouver, Canada 24-29 July, 2016) p. 5186-5194
- Publisher Link
- http://dx.doi.org/10.1109/CEC.2016.7748347
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- conference paper
- Date
- 2016
- Description
- 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.
- Subject
- feature selection; Malicious web domains; web security; classification; machine learning
- Identifier
- http://hdl.handle.net/1959.13/1349117
- Identifier
- uon:30340
- Identifier
- ISBN:9781509006236
- Language
- eng
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