Malicious web domain identification using online credibility and performance data by considering the class imbalance issue
Industrial Management & Data Systems
ISSN: 0263-5577
Article publication date: 4 December 2018
Issue publication date: 29 March 2019
Abstract
Purpose
Malicious web domain identification is of significant importance to the security protection of internet users. With online credibility and performance data, the purpose of this paper to investigate the use of machine learning techniques for malicious web domain identification by considering the class imbalance issue (i.e. there are more benign web domains than malicious ones).
Design/methodology/approach
The authors propose an integrated resampling approach to handle class imbalance by combining the synthetic minority oversampling technique (SMOTE) and particle swarm optimisation (PSO), a population-based meta-heuristic algorithm. The authors use the SMOTE for oversampling and PSO for undersampling.
Findings
By applying eight well-known machine learning classifiers, the proposed integrated resampling approach is comprehensively examined using several imbalanced web domain data sets with different imbalance ratios. Compared to five other well-known resampling approaches, experimental results confirm that the proposed approach is highly effective.
Practical implications
This study not only inspires the practical use of online credibility and performance data for identifying malicious web domains but also provides an effective resampling approach for handling the class imbalance issue in the area of malicious web domain identification.
Originality/value
Online credibility and performance data are applied to build malicious web domain identification models using machine learning techniques. An integrated resampling approach is proposed to address the class imbalance issue. The performance of the proposed approach is confirmed based on real-world data sets with different imbalance ratios.
Keywords
Acknowledgements
This work was supported by the Natural Science Foundation of China (Grant Nos 71571080 and 71601147), the Fundamental Research Funds for the Central Universities (No. 104-413000017) and the China Postdoctoral Science Foundation (No. 2015M582280). The first author would like to acknowledge the International Research Visiting Fellowship from The University of Newcastle, which allowed him to spend two months in Australia for this work. The authors would also like to thank the handling editor and two anonymous reviewers for their valuable comments and suggestions on the previous versions of this paper.
Citation
Hu, Z., Chiong, R., Pranata, I., Bao, Y. and Lin, Y. (2019), "Malicious web domain identification using online credibility and performance data by considering the class imbalance issue", Industrial Management & Data Systems, Vol. 119 No. 3, pp. 676-696. https://doi.org/10.1108/IMDS-02-2018-0072
Publisher
:Emerald Publishing Limited
Copyright © 2018, Emerald Publishing Limited