A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
An Introduction to Kolmogorov Complexity and Its Applications
An Introduction to Kolmogorov Complexity and Its Applications
Identifying suspicious URLs: an application of large-scale online learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Beyond blacklists: learning to detect malicious web sites from suspicious URLs
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Phishnet: predictive blacklisting to detect phishing attacks
INFOCOM'10 Proceedings of the 29th conference on Information communications
A universal algorithm for sequential data compression
IEEE Transactions on Information Theory
Compression of individual sequences via variable-rate coding
IEEE Transactions on Information Theory
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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.