Identifying suspicious URLs

Justin Ma, Lawrence K. Saul, Stefan Savage, Geoffrey M. Voelker
2009 Proceedings of the 26th Annual International Conference on Machine Learning - ICML '09  
This paper explores online learning approaches for detecting malicious Web sites (those involved in criminal scams) using lexical and host-based features of the associated URLs. We show that this application is particularly appropriate for online algorithms as the size of the training data is larger than can be efficiently processed in batch and because the distribution of features that typify malicious URLs is changing continuously. Using a real-time system we developed for gathering URL
more » ... gathering URL features, combined with a real-time source of labeled URLs from a large Web mail provider, we demonstrate that recentlydeveloped online algorithms can be as accurate as batch techniques, achieving classification accuracies up to 99% over a balanced data set.
doi:10.1145/1553374.1553462 dblp:conf/icml/MaSSV09 fatcat:idj6rmckorchvjvpbeubykmaba