1 research outputs found
Malicious Web Domain Identification using Online Credibility and Performance Data by Considering the Class Imbalance Issue
Purpose: Malicious web domain identification is of significant importance to
the security protection of Internet users. With online credibility and
performance data, this paper aims to investigate the use of machine learning
tech-niques 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: We propose an integrated resampling approach to
handle class imbalance by combining the Synthetic Minority Over-sampling
TEchnique (SMOTE) and Particle Swarm Optimisation (PSO), a population-based
meta-heuristic algorithm. We use the SMOTE for over-sampling and PSO for
under-sampling. Findings: By applying eight well-known machine learning
classifiers, the proposed integrated resampling approach is comprehensively
examined using several imbalanced web domain datasets with different imbalance
ratios. Com-pared 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
imbal-ance issue in the area of malicious web domain identification.
Originality/value: Online credibility and performance data is applied to build
malicious web domain identification models using machine learning techniques.
An integrated resampling approach is proposed to address the class im-balance
issue. The performance of the proposed approach is confirmed based on
real-world datasets with different imbalance ratios.Comment: 20 page