文章基本信息

标题：Malicious Javascript Detection based on Clustering Techniques
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作者：Nguyen Hong Son ; Ha Thanh Dung
期刊名称：International Journal of Network Security & Its Applications
印刷版ISSN：0975-2307
电子版ISSN：0974-9330
出版年度：2021
卷号：13
期号：6
页码：11-21
语种：English
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Malicious JavaScript code is still a problem for website and web users. The complication and equivocation of this code make the detection which is based on signatures of antivirus programs becomes ineffective. So far, the alternative methods using machine learning have achieved encouraging results, and have detected malicious JavaScript code with high accuracy. However, according to the supervised learning method, the models, which are introduced, depend on the number of labeled symbols and require significant computational resources to activate. The rapid growth of malicious JavaScript is a real challenge to the solutions based on supervised learning due to the lacking of experience in detecting new forms of malicious JavaScript code. In this paper, we deal with the challenge by the method of detecting malicious JavaScript based on clustering techniques. The known symbols that will be analyzed, the characteristics which are extracted, and a detection processing technique applied on output clusters are included in the model. This method is not computationally complicated, as well as the typical case experiments gave positive results; specifically, it has detected new forms of malicious JavaScript code.
关键词：Malicious JavaScript;Detection model;K-means clustering algorithm;Prediction support parameter