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Classification features for attack detection in collaborative recommender systems

Published: 20 August 2006 Publication History

Abstract

Collaborative recommender systems 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 identify types of attacks and study mechanisms for recognizing and defeating them. In this paper, we propose and study different attributes derived from user profiles for their utility in attack detection. We show that a machine learning classification approach that includes attributes derived from attack models is more successful than more generalized detection algorithms previously studied.

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        cover image ACM Conferences
        KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
        August 2006
        986 pages
        ISBN:1595933395
        DOI:10.1145/1150402
        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: 20 August 2006

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        Author Tags

        1. attack detection
        2. collaborative filtering
        3. recommender systems
        4. robustness

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        View all
        • (2025)User similarity-based graph convolutional neural network for shilling attack detectionApplied Intelligence10.1007/s10489-025-06254-255:5Online publication date: 17-Jan-2025
        • (2024)Unsupervised contaminated user profile identification against shilling attack in recommender systemIntelligent Data Analysis10.3233/IDA-23057528:6(1411-1426)Online publication date: 15-Nov-2024
        • (2024)Manipulating Recommender Systems: A Survey of Poisoning Attacks and CountermeasuresACM Computing Surveys10.1145/367732857:1(1-39)Online publication date: 7-Oct-2024
        • (2024)Toward Adversarially Robust Recommendation From Adaptive Fraudster DetectionIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.332787619(907-919)Online publication date: 2024
        • (2024)Detecting the adversarially-learned injection attacks via knowledge graphsInformation Systems10.1016/j.is.2024.102419125(102419)Online publication date: Nov-2024
        • (2024)Detecting Unknown Shilling Attacks in Recommendation SystemsWireless Personal Communications10.1007/s11277-024-11401-y137:1(259-286)Online publication date: 4-Jul-2024
        • (2024)Enhancing popSAD: A New Approach to Shilling Attack Detection in Collaborative RecommendersProceedings of 4th International Conference on Frontiers in Computing and Systems10.1007/978-981-97-2614-1_4(51-62)Online publication date: 5-Jul-2024
        • (2024)Trust-Centric and Attack-Resistant Recommender SystemRecommender Systems: Algorithms and their Applications10.1007/978-981-97-0538-2_8(81-100)Online publication date: 12-Jun-2024
        • (2024)Learning How to Rank and Collecting User BehaviorRecommender Systems: Algorithms and their Applications10.1007/978-981-97-0538-2_5(39-54)Online publication date: 12-Jun-2024
        • (2024)Detection of Shilling Attack with Support Vector Machines Using OversamplingScience, Engineering Management and Information Technology10.1007/978-3-031-72287-5_13(215-230)Online publication date: 12-Sep-2024
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