Malicious URL detection based on Kolmogorov complexity estimation

Hsing Kuo Pao, Yan Lin Chou, Yuh-Jye Lee

研究成果: Conference contribution同行評審

9 引文 斯高帕斯(Scopus)

摘要

Malicious URL detection has drawn a significant research attention in recent years. It is helpful if we can simply use the URL string to make precursory judgment about how dangerous a website is. By doing that, we can save efforts on the website content analysis and bandwidth for content retrieval. We propose a detection method that is based on an estimation of the conditional Kolmogorov complexity of URL strings. To overcome the incomputability of Kolmogorov complexity, we adopt a compression method for its approximation, called conditional Kolmogorov measure. As a single significant feature for detection, we can achieve a decent performance that can not be achieved by any other single feature that we know. Moreover, the proposed Kolmogorov measure can work together with other features for a successful detection. The experiment has been conducted using a private dataset from a commercial company which can collect more than one million unclassified URLs in a typical hour. On average, the proposed measure can process such hourly data in less than a few minutes.

原文English
主出版物標題Proceedings - 2012 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2012
頁面380-387
頁數8
DOIs
出版狀態Published - 1 十二月 2012
事件2012 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2012 - Macau, China
持續時間: 4 十二月 20127 十二月 2012

出版系列

名字Proceedings - 2012 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2012

Conference

Conference2012 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2012
國家China
城市Macau
期間4/12/127/12/12

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