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“Profile Injection Attack Detection for Securing Collaborative Recommender Systems” by Chad Williams. Masters Thesis, Department of Computer Science, DePaul University, June 2006. Technical Report No. 06-014.
Researchers have shown that collaborative recommender systems, the most common type of web personalization system, are highly vulnerable to attack. Attackers can use automated means to inject a large number of biased profiles into such a system, resulting in recommendations that favor or disfavor given items. Since collaborative recommender systems must be open to user input, it is difficult to design a system that cannot be so attacked. Researchers studying robust recommendation have therefore begun to study mechanisms for recognizing and defeating attacks. In prior work, we have introduced a variety of attributes designed to detect profile injection attacks and evaluated their combined classification performance against several well studied attack models using supervised classification techniques. In this paper, we propose and study the impact the dimensions of attack type, attack intent, filler size, and attack size have on the effectiveness of such a detection scheme. We conclude by experimentally exploring the weaknesses of a detection scheme based on supervised classification, and techniques that can be combined with this approach to address these vulnerabilities.
Keywords: attack detection, attack models, bias profile injection, collaborative filtering, pattern recognition, profile classification, recommender systems, shilling
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BibTeX entry:
@mastersthesis{W06,
author = {Chad Williams},
title = {Profile Injection Attack Detection for Securing Collaborative
Recommender Systems},
school = {Department of Computer Science, DePaul University},
type = {Masters Thesis},
month = jun,
year = {2006},
note = {Technical Report No. 06-014},
url =
{http://www.cdm.depaul.edu/research/Pages/Abstracts/2006/TR06-014.aspx}
}