short-paper

Quick and accurate attack detection in recommender systems through user attributes

Authors:

Mehmet Aktukmak,

Ismail UysalAuthors Info & Claims

RecSys '19: Proceedings of the 13th ACM Conference on Recommender Systems

Pages 348 - 352

https://doi.org/10.1145/3298689.3347050

Published: 10 September 2019 Publication History

Abstract

Malicious profiles have been a credible threat to collaborative recommender systems. Attackers provide fake item ratings to systematically manipulate the platform. Attack detection algorithms can identify and remove such users by observing rating distributions. In this study, we aim to use the user attributes as an additional information source to improve the accuracy and speed of attack detection. We propose a probabilistic factorization model which can embed mixed data type user attributes and observed ratings into a latent space to generate anomaly statistics for new users. To identify the persistent outliers in the system, we also propose a sequential attack detection algorithm to enable quick and accurate detection based on the probabilistic model learned from genuine users. The proposed model demonstrates significant improvements in both accuracy and speed when compared to baseline algorithms on a popular benchmark dataset.

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Cited By

Nguyen TQuoc Viet hung NNguyen THuynh TNguyen TWeidlich MYin H(2024)Manipulating Recommender Systems: A Survey of Poisoning Attacks and CountermeasuresACM Computing Surveys10.1145/367732857:1(1-39)Online publication date: 7-Oct-2024
https://dl.acm.org/doi/10.1145/3677328
Ge YLiu SFu ZTan JLi ZXu SLi YXian YZhang Y(2024)A Survey on Trustworthy Recommender SystemsACM Transactions on Recommender Systems10.1145/36528913:2(1-68)Online publication date: 13-Apr-2024
https://dl.acm.org/doi/10.1145/3652891
Tariq MTahir BZhou Z(2024)Secure and Enhanced Online Recommendations: A Federated Intelligence ApproachIEEE Transactions on Consumer Electronics10.1109/TCE.2023.333515670:1(2500-2507)Online publication date: Feb-2024
https://doi.org/10.1109/TCE.2023.3335156
Show More Cited By

Index Terms

Quick and accurate attack detection in recommender systems through user attributes
1. Security and privacy
  1. Intrusion/anomaly detection and malware mitigation

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cover image ACM Other conferences

RecSys '19: Proceedings of the 13th ACM Conference on Recommender Systems

September 2019

635 pages

ISBN:9781450362436

DOI:10.1145/3298689

General Chairs:
Toine Bogers
Aalborg University Copenhagen, Denmark
,
Alan Said
University of Gothenburg, Sweden
,
Program Chairs:
Peter Brusilovsky
University of Pittsburgh
,
Domonkos Tikk
Gravity R&D, Hungary

Copyright © 2019 ACM.

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 September 2019

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RecSys '19

RecSys '19: Thirteenth ACM Conference on Recommender Systems

September 16 - 20, 2019

Copenhagen, Denmark

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RecSys '19 Paper Acceptance Rate 36 of 189 submissions, 19%;

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

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Cited By

Nguyen TQuoc Viet hung NNguyen THuynh TNguyen TWeidlich MYin H(2024)Manipulating Recommender Systems: A Survey of Poisoning Attacks and CountermeasuresACM Computing Surveys10.1145/367732857:1(1-39)Online publication date: 7-Oct-2024
https://dl.acm.org/doi/10.1145/3677328
Ge YLiu SFu ZTan JLi ZXu SLi YXian YZhang Y(2024)A Survey on Trustworthy Recommender SystemsACM Transactions on Recommender Systems10.1145/36528913:2(1-68)Online publication date: 13-Apr-2024
https://dl.acm.org/doi/10.1145/3652891
Tariq MTahir BZhou Z(2024)Secure and Enhanced Online Recommendations: A Federated Intelligence ApproachIEEE Transactions on Consumer Electronics10.1109/TCE.2023.333515670:1(2500-2507)Online publication date: Feb-2024
https://doi.org/10.1109/TCE.2023.3335156
WANG CYe JWang WGao CFeng FHe X(2023)RecAD: Towards A Unified Library for Recommender Attack and DefenseProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3609490(234-244)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3609490
Nguyen Thanh TQuach NNguyen THuynh TVu VNguyen PJo JNguyen Q(2023)Poisoning GNN-based Recommender Systems with Generative Surrogate-based AttacksACM Transactions on Information Systems10.1145/356742041:3(1-24)Online publication date: 7-Feb-2023
https://dl.acm.org/doi/10.1145/3567420
Wu CLian DGe YZhu ZChen E(2023)Influence-Driven Data Poisoning for Robust Recommender SystemsIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2023.3274759(1-17)Online publication date: 2023
https://doi.org/10.1109/TPAMI.2023.3274759
Qian FYuan BChen HChen JLian DZhao S(2023)Enhancing the Transferability of Adversarial Examples Based on Nesterov Momentum for Recommendation SystemsIEEE Transactions on Big Data10.1109/TBDATA.2023.32486269:5(1276-1287)Online publication date: Oct-2023
https://doi.org/10.1109/TBDATA.2023.3248626
Zayed RIbrahim LHefny HSalman HAlMohimeed A(2023)Experimental and Theoretical Study for the Popular Shilling Attacks Detection Methods in Collaborative Recommender SystemIEEE Access10.1109/ACCESS.2023.328940411(79358-79369)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3289404
Wu CLian DGe YZhu ZChen EZhu FChin Ooi BMiao CWang HSkrypnyk IHsu WChawla S(2021)Triple Adversarial Learning for Influence based Poisoning Attack in Recommender SystemsProceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining10.1145/3447548.3467335(1830-1840)Online publication date: 14-Aug-2021
https://dl.acm.org/doi/10.1145/3447548.3467335
Wu CLian DGe YZhu ZChen EYuan SDiaz FShah CSuel TCastells PJones RSakai T(2021)Fight Fire with Fire: Towards Robust Recommender Systems via Adversarial Poisoning TrainingProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3462914(1074-1083)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3404835.3462914
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