Improving Digest-Based Collaborative Spam Detection

Spam is usually sent in bulk. A bulk mailing consists of many copies of the same original spam message, each sent to a different recipient. The copies are usually obfuscated, i.e. modified a bit in order to look different from each other. In collaborative spam filtering it is important to determine which emails belong to the same bulk. This allows, after observing an initial portion of a bulk, for the bulkiness scores to be assigned to the remaining emails from the same bulk. This also allows the individual evidence of spamminess to be joined, if such evidence is generated by collaborating filters or users for some of the emails from an initial portion of the bulk. Then, the observed bulkiness and the estimated spamminess of a bulk can be used to better filter the remaining emails from the same bulk. The work by Damiani et al. [2] ("open-digest paper") is well know and often cited for its positive findings about the properties of a digest-based collaborative spam detection technique. The technique produces similar digests out of similar emails, and uses them to find out which emails belong to the same bulk. Based on the experimental evaluation, the paper suggests that the technique provides bulk-spam detection that is robust to increased obfuscation efforts by spammers, and low miss-detection of good emails. We first repeat and extend some of the open-digest paper [2] experiments, using the simplest spammer model from that paper. We find that the conclusions of the open-digest paper are rather miss- leading. Then we propose and evaluate, under the same spammer model, a modified version of the original digest technique. The modified version greatly improves the resistance of spam detection against increased obfuscation effort by spammers, while keeping miss-detection of good emails at a similar level. Based on the observed results, we discuss possible additional modifications and algorithms that could be added on top of the modified digest technique to further improve its filtering performance.