Review: A review of machine learning approaches to Spam filtering
Expert Systems with Applications: An International Journal
Towards Proactive Spam Filtering (Extended Abstract)
DIMVA '09 Proceedings of the 6th International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment
Detection of spam hosts and spam bots using network flow traffic modeling
LEET'10 Proceedings of the 3rd USENIX conference on Large-scale exploits and emergent threats: botnets, spyware, worms, and more
Longtime behavior of harvesting spam bots
Proceedings of the 2012 ACM conference on Internet measurement conference
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Many URL-based spam filters rely on "white" and "black" lists to classify email. The authors' proposed URL-based spam filter instead analyzes URL statistics to dynamically calculate the probabilities of whether email with specific URLs are spam or legitimate, and then classifies them accordingly.