Biblio

We propose a new spam detection approach based solely on meta data features gained from email headers. The approach achieves above 99 % classification accuracy on the CSDMC2010 dataset, which matches or surpasses state-of-the-art spam classifiers. We utilize a static set of engineered features, supplemented with automatically extracted features. The approach is just as effective for spam detection in end-to-end encryption, as our feature set remains unchanged for encrypted emails. In contrast to most established spam detectors, we disregard the email body completely and can therefore deliver very high classification speeds, as computationally expensive text preprocessing is not necessary.