  This work addresses challenges related to attacks on social tagging systems,
which often comes in a form of malicious annotations or profile injection
attacks. In particular, we study various countermeasures against two types of
threats for such systems, the Overload and the Piggyback attacks. The studied
countermeasures include baseline classifiers such as, Naive Bayes filter and
Support Vector Machine, as well as a deep learning-based approach. Our
evaluation performed over synthetic spam data, generated from del.icio.us,
shows that in most cases, the deep learning-based approach provides the best
protection against threats.
