文章基本信息

标题：Application of Unsupervised Learning for Detection Cross-site Scripting (XSS) Security Breaches
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作者：Paloma Symmonds ; Ali Alharbi ; Ephraim Nielson 等
期刊名称：International Journal of Computer and Information Technology
印刷版ISSN：2279-0764
出版年度：2017
卷号：6
期号：6
页码：277-285
出版社：International Journal of Computer and Information Technology
摘要：Advancements of technology in the field of networking and computing bring with it a heightened importance for cyber security. Currently, legal forms of identification, financial information, and scientific data rely on this technology. Moreover, as more dependence on cloud computing, data-base storage, and online banking is applied keeping sensitive information secure is paramount. JavaScript Cross-site based attacks continue to be the most prevalent cause of compromised information. Here, we demonstrate the feasibility of using unsupervised learning algorithms to detect attacks as part of the Intrusion Detection System for malicious cross scripts with attacked web sites. Our contribution is domain-based, in order to track changes of the interaction. Profiles are made from webcrawled pages and parsed according to key scripting features. The detection is done when scripting deviates from the main clustering of those clean profiles data gathered.
关键词：Cybersecurity; unsupervised neural networks; cyber;attacks; cross;site scripting; XSS; anomaly detection