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research-article

Spotting anomalous ratings for rating systems by analyzing target users and items

Authors:

Yuan YangAuthors Info & Claims

Neurocomputing, Volume 240, Issue C

Pages 25 - 46

https://doi.org/10.1016/j.neucom.2017.02.052

Published: 31 May 2017 Publication History

Abstract

Online rating systems play an important role in recommender systems. Collaborative filtering recommender systems are highly vulnerable to shilling attacks in reality. Although attack detection based on the attacks have been extensively researched over the last decade, the studies on this issue have not reached an end. Furthermore, only using the existing features is not easy to improve their detection performance. In this paper, we present an unsupervised detection method to defend such attacks, which consists of two stages. Based on the existing features of user and item, more effective features are selected using adaptive structure learning which takes advantage of adaptive local and global structure learning. In the first stage, suspected users are determined by exploiting a density-based clustering method based on the selected features. Then, the selected features of item are applied to find out suspicious items in order to further spot the concerned attackers based on the result of the first stage. Finally, the attackers can be detected. Extensive experiments on the MovieLens-100K dataset demonstrate the effectiveness of the proposed approach as compare to competing methods. It is noteworthy that discovering interesting findings including anomalous ratings and items on Amazon dataset also is investigated.

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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
Singh PPramanik PSinhababu NChoudhury P(2024)Detecting Unknown Shilling Attacks in Recommendation SystemsWireless Personal Communications: An International Journal10.1007/s11277-024-11401-y137:1(259-286)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s11277-024-11401-y
Yang ZSun QLiu ZYan JZhang Y(2022)Rating behavior evaluation and abnormality forensics analysis for injection attack detectionJournal of Intelligent Information Systems10.1007/s10844-021-00689-y59:1(93-119)Online publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1007/s10844-021-00689-y
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Published In

cover image Neurocomputing

Neurocomputing Volume 240, Issue C

May 2017

201 pages

ISSN:0925-2312

Issue’s Table of Contents

Copyright © Elsevier B.V.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 31 May 2017

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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
Singh PPramanik PSinhababu NChoudhury P(2024)Detecting Unknown Shilling Attacks in Recommendation SystemsWireless Personal Communications: An International Journal10.1007/s11277-024-11401-y137:1(259-286)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s11277-024-11401-y
Yang ZSun QLiu ZYan JZhang Y(2022)Rating behavior evaluation and abnormality forensics analysis for injection attack detectionJournal of Intelligent Information Systems10.1007/s10844-021-00689-y59:1(93-119)Online publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1007/s10844-021-00689-y
Yang ZSun QZhang YZhu LJi W(2020)Inference of Suspicious Co-Visitation and Co-Rating Behaviors and Abnormality Forensics for Recommender SystemsIEEE Transactions on Information Forensics and Security10.1109/TIFS.2020.297702315(2766-2781)Online publication date: 24-Mar-2020
https://dl.acm.org/doi/10.1109/TIFS.2020.2977023
Xu YZhang F(2019)Detecting shilling attacks in social recommender systems based on time series analysis and trust featuresKnowledge-Based Systems10.1016/j.knosys.2019.04.012178:C(25-47)Online publication date: 15-Aug-2019
https://dl.acm.org/doi/10.1016/j.knosys.2019.04.012
Guo LLiang JZhu YLuo YSun LZheng X(2019)Collaborative filtering recommendation based on trust and emotionJournal of Intelligent Information Systems10.1007/s10844-018-0517-453:1(113-135)Online publication date: 20-Sep-2019
https://dl.acm.org/doi/10.1007/s10844-018-0517-4
Hao YZhang PZhang F(2018)Multiview Ensemble Method for Detecting Shilling Attacks in Collaborative Recommender SystemsSecurity and Communication Networks10.1155/2018/81746032018Online publication date: 11-Oct-2018
https://dl.acm.org/doi/10.1155/2018/8174603
Yang ZSun QZhang YZhang B(2018)Uncovering anomalous rating behaviors for rating systemsNeurocomputing10.1016/j.neucom.2018.05.001308:C(205-226)Online publication date: 25-Sep-2018
https://dl.acm.org/doi/10.1016/j.neucom.2018.05.001

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