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. Inthis 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.

中文翻译：

---

一种用于恶意网络域识别的模糊加权方法

恶意 Web 域对在线用户的隐私和安全构成严重威胁，会导致金钱损失、私人信息被盗和恶意软件攻击等。近年来，机器学习方法已被广泛用作识别恶意网络域的预测模型。在这项研究中，我们提出了一种用于恶意网络域识别的模糊加权最小二乘支持向量机（FW-LS-SVM）模型。在我们提出的模型中，考虑到不同样本可能具有不同的重要性，对每个数据样本应用模糊加权操作。这种模糊加权操作还能够通过为误差约束分配权重来减轻噪声数据的影响并提高模型的鲁棒性。为了比较，我们的实验包括三个常用的单一机器学习分类器和三个广泛使用的集成模型，以评估我们提出的 FW-LS-SVM 及其集成版本的性能。超链接指标和基于统一资源定位器的特征用于训练预测模型。实验结果表明，我们提出的方法在识别恶意 Web 域方面非常有效，优于正在比较的完善的单一和集成模型。