Malicious URL Detection Based on Kolmogorov Complexity Estimation

Abstract

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.