A fuzzy-weighted approach for malicious web domain identification

Abstract

Malicious web domains represent a serious threat to online users’ privacy and security, causing monetary loss, theft of private information, and malware attacks, among others. In recent years, machine learning methods have been widely used as prediction models to identify malicious web domains. In this study, we propose a Fuzzy-Weighted Least Squares Support Vector Machine (FW-LS-SVM) model for malicious web domain identification. In our proposed model, a fuzzy-weighted operation is applied to each data sample considering the fact that different samples may have different importance. This fuzzy-weighted operation is also able to alleviate the influence of noise data and improve the model’s robustness by assigning weights to error constraints. For comparison purposes, three commonly used single machine learning classifiers and three widely used ensemble models are included in our experiments, in order to assess the performance of our proposed FW-LS-SVM and its ensemble version. Hyperlink indicators and uniform resource locator-based features are used to train the prediction models. Experimental results show that our proposed approach is highly effective in identifying malicious web domains, outperforming the well-established single and ensemble models being compared.