This work addresses the challenges related to attacks on collaborative 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 such attacks for social tagging systems, the Overload attack and the Piggyback attack. The countermeasure schemes studied here include baseline classifiers such as, Naive Bayes filter and Support Vector Machine, as well as a Deep Learning approach. Our evaluation performed over synthetic spam data generated from del.icio.us dataset, shows that in most cases, Deep Learning can outperform the classical solutions, providing high-level protection against threats.
The issue of security in tag-based RS in not new, but it has so far been mainly approached by solutions associated with spam detection. As per @cite_8 , anti-spam approaches in social networks are divided into 3 main categories: a) , such as CAPTCHAs , b) approaches which demote spam in search queries, and c) solutions which aim to detect and isolate any potentially threatening entities, such as a user or a resource. The focus of our work is on the latter type only.
@cite_8
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In recent years, social Web sites have become important components of the Web. With their success, however, has come a growing influx of spam. If left unchecked, spam threatens to undermine resource sharing, interactivity, and openness. This article surveys three categories of potential countermeasures - those based on detection, demotion, and prevention. Although many of these countermeasures have been proposed before for email and Web spam, the authors find that their applicability to social Web sites differs.