research-article

Re-scale AdaBoost for attack detection in collaborative filtering recommender systems

Knowledge-Based Systems, Volume 100, Issue C

Pages 74 - 88

https://doi.org/10.1016/j.knosys.2016.02.008

Published: 15 May 2016 Publication History

Abstract

Collaborative filtering recommender systems (CFRSs) are the key components of successful E-commerce systems. However, CFRSs are highly vulnerable to "shilling" attacks or "profile injection" attacks due to its openness. Since the size of attackers is usually far smaller than genuine users, conventional supervised learning based detection methods could be too "dull" to handle such imbalanced classification. In this paper, we improve detection performance from following two aspects. Firstly, we extract well-designed features from user profiles based on the statistical properties of the diverse attack models, making hard detection scenarios become easier to perform. Then, refer to the general idea of re-scale Boosting (RBoosting) and AdaBoost, we apply a variant of AdaBoost, called the re-scale AdaBoost (RAdaBoost) as our detection method based on the extracted features. Finally, a series of experiments on the MovieLens-100K dataset are conducted to demonstrate the outperformance of RAdaBoost over other competing techniques such as SVM, kNN and AdaBoost.

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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 Systems10.1145/3652891Online publication date: 13-Apr-2024
https://doi.org/10.1145/3652891
Zhang KCao QWu YSun FShen HCheng X(2024)Improving the Shortest Plank: Vulnerability-Aware Adversarial Training for Robust Recommender SystemProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688120(680-689)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688120
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cover image Knowledge-Based Systems

Knowledge-Based Systems Volume 100, Issue C

May 2016

212 pages

ISSN:0950-7051

Issue’s Table of Contents

Copyright © Elsevier B.V.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 15 May 2016

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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 Systems10.1145/3652891Online publication date: 13-Apr-2024
https://doi.org/10.1145/3652891
Zhang KCao QWu YSun FShen HCheng X(2024)Improving the Shortest Plank: Vulnerability-Aware Adversarial Training for Robust Recommender SystemProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688120(680-689)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688120
Zhang KCao QWu YSun FShen HCheng XHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)LoRec: Combating Poisons with Large Language Model for Robust Sequential RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657684(1733-1742)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657684
Li XWang Z(2023)RETRACTED ARTICLE: Support attack detection algorithm for recommendation system based on deep learningEURASIP Journal on Wireless Communications and Networking10.1186/s13638-023-02269-w2023:1Online publication date: 20-Jul-2023
https://dl.acm.org/doi/10.1186/s13638-023-02269-w
Tariq MTahir BZhou Z(2023)Secure and Enhanced Online Recommendations: A Federated Intelligence ApproachIEEE Transactions on Consumer Electronics10.1109/TCE.2023.333515670:1(2500-2507)Online publication date: 28-Nov-2023
https://dl.acm.org/doi/10.1109/TCE.2023.3335156
Jain KJindal R(2023)Sampling and noise filtering methods for recommender systemsEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.106129122:COnline publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1016/j.engappai.2023.106129
Wang QWu CLian DChen E(2023)Securing recommender system via cooperative trainingWorld Wide Web10.1007/s11280-023-01214-726:6(3915-3943)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1007/s11280-023-01214-7
Nguyen Hoai Nam L(2022)Incorporating textual reviews in the learning of latent factors for recommender systemsElectronic Commerce Research and Applications10.1016/j.elerap.2022.10113352:COnline publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1016/j.elerap.2022.101133
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
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