Email spam filters are commonly trained on a sample of spam and ham (non-spam) messages. We investigate the effect on filter performance of using samples of spam and ham messag...
Web mail providers rely on users to “vote” to quickly and collaboratively identify spam messages. Unfortunately, spammers have begun to use bots to control large collections o...
Anirudh Ramachandran, Anirban Dasgupta, Nick Feams...
By feeding personal e-mails into the training set, personalized content-based spam filters are believed to classify e-mails in higher accuracy. However, filters trained by both sp...
In this paper we consider the approach to image spam filtering based on using image classifiers aimed at discriminating between ham and spam images, previously proposed by other a...
Giorgio Fumera, Fabio Roli, Battista Biggio, Ignaz...
We describe a very simple technique for discriminatively training a spam filter. Our results on the TREC Enron spam corpus would have been the best for the Ham at .1% measure, and...