Securing Tag-based recommender systems against profile injection attacks: A comparative study. (Extended Report)
This work addresses security issues for social tagging systems, but it is incremental as it compares existing methods on synthetic data.
The paper tackled the problem of profile injection attacks in tag-based recommender systems by evaluating countermeasures against Overload and Piggyback attacks, finding that deep learning outperforms classical methods like Naive Bayes and SVM in most cases, providing high-level protection.
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.