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Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
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
Identifying spam link generators for monitoring emerging web spam
Proceedings of the 4th workshop on Information credibility
Beyond online aggregation: parallel and incremental data mining with online Map-Reduce
Proceedings of the 2010 Workshop on Massive Data Analytics on the Cloud
Lexical feature based phishing URL detection using online learning
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SKIF: a data imputation framework for concept drifting data streams
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The Journal of Machine Learning Research
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Fluxing botnet command and control channels with URL shortening services
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AISC '13 Proceedings of the Eleventh Australasian Information Security Conference - Volume 138
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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 features, combined with a real-time source of labeled URLs from a large Web mail provider, we demonstrate that recently-developed online algorithms can be as accurate as batch techniques, achieving classification accuracies up to 99% over a balanced data set.