The security threats from malicious web pages have become a hot topic for cyber security. One goal pursued by current research is to identify malicious web pages quickly, accurately, and efficiently. Considering the high detection costs and potential dimensionality curse of malicious webpage detection, in this article, we proposes a detection framework based on feature optimization and hybrid classification. It provides three properties: more new malicious webpage features, information gain-based feature selection method, and integrating multiple machine learning method. A comprehensive experimental evaluation demonstrates that the proposed framework has remarkable advantages in aspects of detection accuracy and detection performance.

中文翻译：

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恶意网页检测的特征优化和混合分类

来自恶意网页的安全威胁已成为网络安全的热门话题。当前研究追求的一个目标是快速、准确、高效地识别恶意网页。考虑到恶意网页检测的高检测成本和潜在的维度诅咒，在本文中，我们提出了一种基于特征优化和混合分类的检测框架。它提供了三个属性：更多新的恶意网页特征、基于信息增益的特征选择方法和集成多种机器学习方法。综合实验评估表明，该框架在检测精度和检测性能方面具有显着优势。