Malicious URL Detection Based on Kolmogorov Complexity Estimation
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....
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| Vydáno v: | 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology Ročník 1; s. 380 - 387 |
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| Hlavní autoři: | , , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
| Vydáno: |
IEEE
01.12.2012
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| Témata: | |
| ISBN: | 9781467360579, 1467360570 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | 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. |
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| ISBN: | 9781467360579 1467360570 |
| DOI: | 10.1109/WI-IAT.2012.258 |