Security

Ensembles in Adversarial Classification for Spam

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Executive Summary

The standard method for combating spam, either in email or on the web, is to train a classifier on manually labeled instances. As the spammers change their tactics, the performance of such classifiers tends to decrease over time. Gathering and labeling more data to periodically retrain the classifier is expensive. This paper presents a method based on an ensemble of classifiers that can detect when its performance might be degrading and retrain itself, all without manual intervention.

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