An efficient malicious webpage static detection framework based on optimized Bayesian and hybrid machine learning

Malicious webpage detection is a crucial work in both theory and practical environment. In practical applications, static detection methods are usually regarded as a priority choice, which can quickly detect unknown malicious web pages and avoid a costly in-depth analysis. However, existing solution of static detection typically has the following problems. For example, a single static detection may lead to a higher false positive rate, and the integrated detection usually has a lower detection efficiency. In this article, we propose an efficient webpage static detection framework, especially considering both the detection efficiency and the detection accuracy. Then, on the basis of the extended feature sets from URL, HTML, and JavaScript, we introduce an optimized naive Bayesian algorithm, in which a novel amplification factor strategy is proposed. Finally, a webpage threat assessment model oriented to general machine learning is presented to achieve the refined detection. Three main properties are provided: high detection efficiency, high detection accuracy, and better applicability. Furthermore, the comprehensive experimental results and comparative analysis is given to show the advantages of the proposed framework.