Shilling attacks against recommender systems: a comprehensive survey

Online vendors employ collaborative filtering algorithms to provide recommendations to their customers so that they can increase their sales and profits. Although recommendation schemes are successful in e-commerce sites, they are vulnerable to shilling or profile injection attacks. On one hand, online shopping sites utilize collaborative filtering schemes to enhance their competitive edge over other companies. On the other hand, malicious users and/or competing vendors might decide to insert fake profiles into the user-item matrices in such a way so that they can affect the predicted ratings on behalf of their advantages. In the past decade, various studies have been conducted to scrutinize different shilling attacks strategies, profile injection attack types, shilling attack detection schemes, robust algorithms proposed to overcome such attacks, and evaluate them with respect to accuracy, cost/benefit, and overall performance. Due to their popularity and importance, we survey about shilling attacks in collaborative filtering algorithms. Giving an overall picture about various shilling attack types by introducing new classification attributes is imperative for further research. Explaining shilling attack detection schemes in detail and robust algorithms proposed so far might open a lead to develop new detection schemes and enhance such robust algorithms further, even propose new ones. Thus, we describe various attack types and introduce new dimensions for attack classification. Detailed description of the proposed detection and robust recommendation algorithms are given. Moreover, we briefly explain evaluation of the proposed schemes. We conclude the paper by discussing various open questions.

[1]  Neil J. Hurley,et al.  Collaborative recommendation: A robustness analysis , 2004, TOIT.

[2]  Robin Burke,et al.  Effectiveness of crawling attacks against web-based recommender systems , 2007, AAAI 2007.

[3]  Paul Resnick,et al.  The information cost of manipulation-resistance in recommender systems , 2008, RecSys '08.

[4]  Fuguo Zhang,et al.  Analysis of Bandwagon and Average Hybrid Attack Model against Trust-based Recommender Systems , 2011, 2011 Fifth International Conference on Management of e-Commerce and e-Government.

[5]  Neil J. Hurley,et al.  Robust Collaborative Recommendation , 2011, Recommender Systems Handbook.

[6]  Paul Resnick,et al.  The influence limiter: provably manipulation-resistant recommender systems , 2007, RecSys '07.

[7]  Lior Rokach,et al.  Recommender Systems Handbook , 2010 .

[8]  Fuguo Zhang,et al.  A Survey of Shilling Attacks in Collaborative Filtering Recommender Systems , 2009, 2009 International Conference on Computational Intelligence and Software Engineering.

[9]  Padraig Cunningham,et al.  Unsupervised retrieval of attack profiles in collaborative recommender systems , 2008, RecSys '08.

[10]  Bamshad Mobasher,et al.  Profile Injection Attack Detection for Securing Collaborative Recommender Systems 1 , 2006 .

[11]  Zheng Chen,et al.  Finding group shilling in recommendation system , 2005, WWW '05.

[12]  Thomas Hofmann,et al.  A Survey of Attack-Resistant Collaborative Filtering Algorithms , 2008, IEEE Data Eng. Bull..

[13]  Jonathan L. Herlocker,et al.  Evaluating collaborative filtering recommender systems , 2004, TOIS.

[14]  Jie Cao,et al.  Semi-SAD: applying semi-supervised learning to shilling attack detection , 2011, RecSys '11.

[15]  Bamshad Mobasher,et al.  Impact of Relevance Measures on the Robustness and Accuracy of Collaborative Filtering , 2007, EC-Web.

[16]  Qingyun Long,et al.  Robust evaluation of binary collaborative recommendation under profile injection attack , 2010, 2010 IEEE International Conference on Progress in Informatics and Computing.

[17]  R. Burke,et al.  Limited Knowledge Shilling Attacks in Collaborative Filtering Systems , 2005 .

[18]  Robin Burke,et al.  Collaborative Recommendation Vulnerability To Focused Bias Injection Attacks , 2005 .

[19]  Chrysanthos Dellarocas,et al.  Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior , 2000, EC '00.

[20]  Fuguo Zhang.  Reverse Bandwagon Profile Inject Attack against Recommender Systems , 2009, 2009 Second International Symposium on Computational Intelligence and Design.

[21]  Bamshad Mobasher,et al.  Crawling Attacks Against Web-based Recommender Systems , 2007, DMIN.

[22]  Neil J. Hurley,et al.  Recommender Systems: Attack Types and Strategies , 2005, AAAI.

[23]  Zunping Cheng,et al.  Effective diverse and obfuscated attacks on model-based recommender systems , 2009, RecSys '09.

[24]  Qiang Zhang,et al.  A Trust-Based Detecting Mechanism against Profile Injection Attacks in Recommender Systems , 2009, 2009 Third IEEE International Conference on Secure Software Integration and Reliability Improvement.

[25]  Bhaskar Mehta.  Unsupervised Shilling Detection for Collaborative Filtering , 2007, AAAI.

[26]  Shoshana Loeb,et al.  Information filtering , 1992, CACM.

[27]  R. Burke,et al.  A Clustering Approach to Unsupervised Attack Detection in Collaborative Recommender Systems , 2022 .

[28]  Neil J. Hurley,et al.  Utility-based neighbourhood formation for efficient and robust collaborative filtering , 2004, EC '04.

[29]  Ambuj Mahanti,et al.  Strategies for Effective Shilling Attacks against Recommender Systems , 2009, PinKDD.

[30]  Zhigang Luo,et al.  Detection of shilling attacks in collaborative filtering recommender systems , 2011, 2011 International Conference of Soft Computing and Pattern Recognition (SoCPaR).

[31]  Bamshad Mobasher,et al.  Model-Based Collaborative Filtering as a Defense against Profile Injection Attacks , 2006, AAAI.

[32]  Bamshad Mobasher,et al.  Detecting Profile Injection Attacks in Collaborative Filtering: A Classification-Based Approach , 2006, WEBKDD.

[33]  Zunping Cheng,et al.  Statistical attack detection , 2009, RecSys '09.

[34]  Robin Burke,et al.  Identifying Attack Models for Secure Recommendation , 2004 .

[35]  Shyhtsun Felix Wu,et al.  Social Manipulation of Online Recommender Systems , 2010, SocInfo.

[36]  Zunping Cheng,et al.  Robustness Analysis of Model-Based Collaborative Filtering Systems , 2009, AICS.

[37]  Thomas Hofmann,et al.  Lies and propaganda: detecting spam users in collaborative filtering , 2007, IUI '07.

[38]  Zunping Cheng,et al.  Analysis of robustness in trust-based recommender systems , 2010, RIAO.

[39]  R. Burke,et al.  Detection of Obfuscated Attacks in Collaborative Recommender Systems 1 , 2006 .

[40]  Fuguo Zhang.  Analysis of Segment Shilling Attack Against Trust Based Recommender Systems , 2008, 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing.

[41]  Neil J. Hurley,et al.  Towards Robust Collaborative Filtering , 2002, AICS.

[42]  Bamshad Mobasher,et al.  Detecting Profile Injection Attacks in Collaborative Recommender Systems , 2006, The 8th IEEE International Conference on E-Commerce Technology and The 3rd IEEE International Conference on Enterprise Computing, E-Commerce, and E-Services (CEC/EEE'06).

[43]  Fuguo Zhang.  Average Shilling Attack against Trust-Based Recommender Systems , 2009, 2009 International Conference on Information Management, Innovation Management and Industrial Engineering.

[44]  Bamshad Mobasher,et al.  Towards Trustworthy Recommender Systems : An Analysis of Attack Models and Algorithm Robustness , 2007 .

[45]  Thomas Hofmann,et al.  Robust collaborative filtering , 2007, RecSys '07.

[46]  Paolo Avesani,et al.  Trust-aware recommender systems , 2007, RecSys '07.

[47]  Paul Resnick,et al.  Manipulation-resistant recommender systems through influence limits , 2008, SECO.

[48]  Eric Horvitz,et al.  Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach , 2000, UAI.

[49]  John Riedl,et al.  Do You Trust Your Recommendations? An Exploration of Security and Privacy Issues in Recommender Systems , 2006, ETRICS.

[50]  Neil J. Hurley,et al.  Promoting Recommendations: An Attack on Collaborative Filtering , 2002, DEXA.

[51]  Bamshad Mobasher,et al.  Evaluating the Impact of Attacks in Collaborative Tagging Environments , 2009, 2009 International Conference on Computational Science and Engineering.

[52]  GeunSik Jo,et al.  Distributed Collaborative Filtering for Robust Recommendations Against Shilling Attacks , 2007, Canadian Conference on AI.

[53]  Michael J. Pazzani,et al.  Collaborative Filtering with the Simple Bayesian Classifier , 2000, PRICAI.

[54]  Michael J. Pazzani,et al.  Improvement of Collaborative Filtering with the Simple Bayesian Classifier 1 , 2002 .

[55]  Bhaskar Mehta,et al.  Attack resistant collaborative filtering , 2008, SIGIR '08.

[56]  Bhaskar Mehta,et al.  Unsupervised strategies for shilling detection and robust collaborative filtering , 2009, User Modeling and User-Adapted Interaction.

[57]  Robin Burke,et al.  Effective Attack Models for Shilling Item-Based Collaborative Filtering Systems , 2005 .

[58]  Ambuj Mahanti,et al.  Improving Prediction Accuracy in Trust-Aware Recommender Systems , 2010, 2010 43rd Hawaii International Conference on System Sciences.

[59]  Zunping Cheng,et al.  Robust Collaborative Recommendation by Least Trimmed Squares Matrix Factorization , 2010, 2010 22nd IEEE International Conference on Tools with Artificial Intelligence.

[60]  Douglas B. Terry,et al.  Using collaborative filtering to weave an information tapestry , 1992, CACM.

[61]  Fillia Makedon,et al.  Attack detection in time series for recommender systems , 2006, KDD '06.

[62]  Neil J. Hurley,et al.  Collaborative Filtering - Safe and Sound? , 2003, ISMIS.

[63]  Benjamin Van Roy,et al.  Manipulation Robustness of Collaborative Filtering Systems , 2009, ArXiv.

[64]  Yan Tang,et al.  An effective recommender attack detection method based on time SFM factors , 2011, 2011 IEEE 3rd International Conference on Communication Software and Networks.

[65]  Ambuj Mahanti,et al.  Filler Item Strategies for Shilling Attacks against Recommender Systems , 2009 .

[66]  Fillia Makedon,et al.  Analysis of a low-dimensional linear model under recommendation attacks , 2006, SIGIR.

[67]  Baoxu Liu,et al.  Attack Detection by Rough Set Theory in Recommendation System , 2010, 2010 IEEE International Conference on Granular Computing.

[68]  Wolfgang Nejdl,et al.  Preventing shilling attacks in online recommender systems , 2005, WIDM '05.

[69]  Benjamin Van Roy,et al.  Manipulation-resistant collaborative filtering systems , 2009, RecSys '09.

[70]  Zhang Fu-guo,et al.  Analysis of Trust-Based E-Commerce Recommender Systems Under Recommendation Attacks , 2007, The First International Symposium on Data, Privacy, and E-Commerce (ISDPE 2007).

[71]  Bamshad Mobasher,et al.  Analysis and Detection of Segment-Focused Attacks Against Collaborative Recommendation , 2005, WEBKDD.

[72]  Robin Burke,et al.  Securing collaborative filtering against malicious attacks through anomaly detection , 2006, AAAI 2006.

[73]  Wenliang Du,et al.  Privacy-Preserving Collaborative Filtering , 2005, Int. J. Electron. Commer..

[74]  张富国.  Preventing Recommendation Attack in Trust-Based Recommender Systems , 2011 .

[75]  Bamshad Mobasher,et al.  Defending recommender systems: detection of profile injection attacks , 2007, Service Oriented Computing and Applications.

[76]  Robin Burke,et al.  The Impact of Attack Profile Classification on the Robustness of Collaborative Recommendation , 2006 .

[77]  Barry Smyth,et al.  Is trust robust?: an analysis of trust-based recommendation , 2006, IUI '06.

[78]  John Riedl,et al.  Shilling recommender systems for fun and profit , 2004, WWW '04.

[79]  Neil J. Hurley,et al.  Efficient and Secure Collaborative Filtering through Intelligent Neighbour Selection , 2004, ECAI.

[80]  David Heckerman,et al.  Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.

[81]  Nathan Oostendorp.  The Copied Item Injection Attack , 2009 .

[82]  John Riedl,et al.  Privacy , Shilling , and The Value of Information in Recommender Systems , 2005 .

[83]  Benjamin Van Roy,et al.  Manipulation Robustness of Collaborative Filtering , 2010, Manag. Sci..

[84]  Barry Smyth,et al.  Collaborative web search: a robustness analysis , 2007, Artificial Intelligence Review.

[85]  Zunping Cheng,et al.  Trading Robustness for Privacy in Decentralized Recommender Systems , 2009, IAAI.

[86]  Bamshad Mobasher,et al.  A Survey of Collaborative Recommendation and the Robustness of Model-Based Algorithms , 2008, IEEE Data Eng. Bull..

[87]  Fuguo Zhang.  Analysis of Love-Hate Shilling Attack Against E-commerce Recommender System , 2010, 2010 International Conference of Information Science and Management Engineering.

[88]  Neil J. Hurley,et al.  Attacking Recommender Systems: A Cost-Benefit Analysis , 2007, IEEE Intelligent Systems.

[89]  Bamshad Mobasher,et al.  Robustness of collaborative recommendation based on association rule mining , 2007, RecSys '07.

[90]  Neil J. Hurley,et al.  An Evaluation of Neighbourhood Formation on the Performance of Collaborative Filtering , 2004, Artificial Intelligence Review.

[91]  Bamshad Mobasher,et al.  Classification features for attack detection in collaborative recommender systems , 2006, KDD '06.

[92]  Neil J. Hurley,et al.  Detecting noise in recommender system databases , 2006, IUI '06.

[93]  Bamshad Mobasher,et al.  Segment-based injection attacks against collaborative filtering recommender systems , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[94]  Bamshad Mobasher,et al.  Attacks and Remedies in Collaborative Recommendation , 2007, IEEE Intelligent Systems.