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

## TL;DR

This paper compares different countermeasures, including classical classifiers and deep learning, for protecting social tagging systems from malicious profile injection attacks like Overload and Piggyback, demonstrating deep learning's superior effectiveness.

## Contribution

It provides a comparative analysis of classical and deep learning methods to defend against profile injection attacks in social tagging systems.

## Key findings

- Deep learning outperforms classical classifiers in attack detection.
- Support Vector Machine and Naive Bayes offer baseline protection.
- Synthetic spam data used for evaluation shows deep learning's robustness.

## Abstract

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.