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Effective diverse and obfuscated attacks on model-based recommender systems

Published: 23 October 2009 Publication History

Abstract

Robustness analysis research has shown that conventional memory-based recommender systems are very susceptible to malicious profile-injection attacks. A number of attack models have been proposed and studied and recent work has suggested that model-based collaborative filtering (CF) algorithms have greater robustness against these attacks. Moreover, to combat such attacks, several attack detection algorithms have been proposed. One that has shown high detection accuracy is based on using principal component analysis (PCA) to cluster attack profiles on the basis that such profiles are highly correlated. In this paper, we argue that the robustness observed in model-based algorithms is due to the fact that the proposed attacks have not targeted the specific vulnerabilities of these algorithms. We discuss how an effective attack targeting model-based algorithms that employ profile clustering can be designed. It transpires that the attack profiles employed in this attack, exhibit low rather than high pair-wise similarities and can easily be obfuscated to avoid PCA-based detection, while remaining effective.

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cover image ACM Conferences
RecSys '09: Proceedings of the third ACM conference on Recommender systems
October 2009
442 pages
ISBN:9781605584355
DOI:10.1145/1639714
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 23 October 2009

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Author Tags

  1. attack
  2. collaborative filtering
  3. detection
  4. obfuscation
  5. recommender systems

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RecSys '09
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RecSys '09: Third ACM Conference on Recommender Systems
October 23 - 25, 2009
New York, New York, USA

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Overall Acceptance Rate 254 of 1,295 submissions, 20%

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  • (2023)Healthy Personalized Recipe Recommendations for Weekly Meal PlanningComputers10.3390/computers1301000113:1(1)Online publication date: 20-Dec-2023
  • (2023)Targeted Shilling Attacks on GNN-based Recommender SystemsProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615073(649-658)Online publication date: 21-Oct-2023
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