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This work addresses the challenges related to attacks on collaborativetagging systems, which often comes in a form of malicious annotations orprofile injection attacks. In particular, we study various countermeasuresagainst two types of such attacks for social tagging systems, the Overloadattack and the Piggyback attack. The countermeasure schemes studied hereinclude baseline classifiers such as, Naive Bayes filter and Support VectorMachine, as well as a Deep Learning approach. Our evaluation performed oversynthetic spam data generated from del.icio.us dataset, shows that in mostcases, Deep Learning can outperform the classical solutions, providinghigh-level protection against threats.

Securing Tag-based recommender systems against profile injection attacks: A comparative study. (Extended Report)