{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T00:31:03Z","timestamp":1773707463144,"version":"3.50.1"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2020,9,14]],"date-time":"2020-09-14T00:00:00Z","timestamp":1600041600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,9,14]],"date-time":"2020-09-14T00:00:00Z","timestamp":1600041600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Intell Rev"],"published-print":{"date-parts":[[2021,3]]},"DOI":"10.1007\/s10462-020-09898-3","type":"journal-article","created":{"date-parts":[[2020,9,14]],"date-time":"2020-09-14T14:02:47Z","timestamp":1600092167000},"page":"2011-2066","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["A survey of attack detection approaches in collaborative filtering recommender systems"],"prefix":"10.1007","volume":"54","author":[{"given":"Fatemeh","family":"Rezaimehr","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9836-9388","authenticated-orcid":false,"given":"Chitra","family":"Dadkhah","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,9,14]]},"reference":[{"issue":"6","key":"9898_CR1","doi-asserted-by":"publisher","first-page":"734","DOI":"10.1109\/TKDE.2005.99","volume":"17","author":"G Adomavicius","year":"2005","unstructured":"Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng 17(6):734\u2013749","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"9898_CR2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-29659-3","volume-title":"Recommender systems","author":"CC Aggarwal","year":"2016","unstructured":"Aggarwal CC (2016) Recommender systems. Springer, Berlin"},{"key":"9898_CR3","doi-asserted-by":"crossref","unstructured":"Aghili G, Shajari M, Khadivi S, Morid MA (2011) Using genre interest of users to detect profile injection attacks in movie recommender systems. In: 2011 10th international conference on machine learning and applications and workshops (ICMLA). IEEE, pp 49\u201352","DOI":"10.1109\/ICMLA.2011.151"},{"key":"9898_CR4","unstructured":"Bhaumik R, Williams C, Mobasher B, Burke R (2006) Securing collaborative filtering against malicious attacks through anomaly detection. In: Proceedings of the 4th workshop on intelligent techniques for web personalization (ITWP\u201906), Boston, p 10"},{"key":"9898_CR5","unstructured":"Bhaumik R, Mobasher B, Burke R (2011) A clustering approach to unsupervised attack detection in collaborative recommender systems. In: Proceedings of the international conference on data mining (DMIN). Citeseer, p 1"},{"key":"9898_CR6","unstructured":"Burke R, Mobasher B, Bhaumik R (2005) Limited knowledge shilling attacks in collaborative filtering systems. In: Proceedings of 3rd international workshop on intelligent techniques for web personalization (ITWP 2005), 19th international joint conference on artificial intelligence (IJCAI 2005), pp 17\u201324"},{"key":"9898_CR7","doi-asserted-by":"publisher","first-page":"113112","DOI":"10.1016\/j.dss.2019.113112","volume":"124","author":"Y Cai","year":"2019","unstructured":"Cai Y, Zhu D (2019) Trustworthy and profit: a new value-based neighbor selection method in recommender systems under shilling attacks. Decis Support Syst 124:113112","journal-title":"Decis Support Syst"},{"issue":"5\u20136","key":"9898_CR8","doi-asserted-by":"publisher","first-page":"729","DOI":"10.1007\/s11280-012-0164-6","volume":"16","author":"J Cao","year":"2013","unstructured":"Cao J, Wu Z, Mao B, Zhang Y (2013) Shilling attack detection utilizing semi-supervised learning method for collaborative recommender system. World Wide Web 16(5\u20136):729\u2013748","journal-title":"World Wide Web"},{"key":"9898_CR9","doi-asserted-by":"publisher","first-page":"963","DOI":"10.1016\/j.protcy.2013.12.444","volume":"10","author":"P Chakraborty","year":"2013","unstructured":"Chakraborty P, Karforma SJPT (2013) Detection of profile-injection attacks in recommender systems using outlier analysis. Procedia Technol 10:963\u2013969","journal-title":"Procedia Technol"},{"key":"9898_CR10","doi-asserted-by":"crossref","unstructured":"Cheng Z, Hurley N (2009) Effective diverse and obfuscated attacks on model-based recommender systems. In: Proceedings of the third ACM conference on recommender systems. ACM, pp 141\u2013148","DOI":"10.1145\/1639714.1639739"},{"key":"9898_CR11","doi-asserted-by":"crossref","unstructured":"Chirita P-A, Nejdl W, Zamfir C (2005) Preventing shilling attacks in online recommender systems. In: Proceedings of the 7th annual ACM international workshop on web information and data management. ACM, pp 67\u201374","DOI":"10.1145\/1097047.1097061"},{"issue":"1","key":"9898_CR12","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1016\/j.dss.2013.01.020","volume":"55","author":"C-Y Chung","year":"2013","unstructured":"Chung C-Y, Hsu P-Y, Huang S-H (2013) \u0392p: a novel approach to filter out malicious rating profiles from recommender systems. Decis Support Syst 55(1):314\u2013325","journal-title":"Decis Support Syst"},{"key":"9898_CR13","doi-asserted-by":"crossref","unstructured":"He F, Wang X, Liu B (2010) Attack detection by rough set theory in recommendation system. In: 2010 IEEE international conference on granular computing (GrC). IEEE, pp 692\u2013695","DOI":"10.1109\/GrC.2010.130"},{"key":"9898_CR14","doi-asserted-by":"crossref","unstructured":"Herlocker JL, Konstan JA, Borchers A, Riedl J (2017) An algorithmic framework for performing collaborative filtering. In: ACM SIGIR forum. ACM, pp 227\u2013234","DOI":"10.1145\/3130348.3130372"},{"key":"9898_CR15","doi-asserted-by":"crossref","unstructured":"Hurley N, Cheng Z, Zhang M (2009) Statistical attack detection. In: Proceedings of the third ACM conference on recommender systems, pp 149\u2013156","DOI":"10.1145\/1639714.1639740"},{"key":"9898_CR16","doi-asserted-by":"crossref","unstructured":"Jamali M, Ester M (2010) A matrix factorization technique with trust propagation for recommendation in social networks. In: Proceedings of the fourth ACM conference on recommender systems. ACM, pp 135\u2013142","DOI":"10.1145\/1864708.1864736"},{"issue":"3","key":"9898_CR17","doi-asserted-by":"publisher","first-page":"609","DOI":"10.1007\/s10115-014-0779-2","volume":"44","author":"A Javari","year":"2015","unstructured":"Javari A, Jalili M (2015) A probabilistic model to resolve diversity\u2013accuracy challenge of recommendation systems. Knowl Inf Syst 44(3):609\u2013627","journal-title":"Knowl Inf Syst"},{"key":"9898_CR18","doi-asserted-by":"crossref","unstructured":"Karthikeyan P, Selvi ST, Neeraja G, Deepika R, Vincent A, Abinaya V (2017) Prevention of shilling attack in recommender systems using discrete wavelet transform and support vector machine. In: 2017 IEEE eighth international conference, pp 99\u2013104","DOI":"10.1109\/ICoAC.2017.7951753"},{"key":"9898_CR19","doi-asserted-by":"crossref","unstructured":"Lam SK, Riedl J (2004) Shilling recommender systems for fun and profit. In: Proceedings of the 13th international conference on world wide web. ACM, pp 393\u2013402","DOI":"10.1145\/988672.988726"},{"issue":"1","key":"9898_CR20","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1287\/ijoc.1100.0440","volume":"24","author":"J-S Lee","year":"2012","unstructured":"Lee J-S, Zhu D (2012) Shilling attack detection\u2014a new approach for a trustworthy recommender system. INFORMS J Comput 24(1):117\u2013131","journal-title":"INFORMS J Comput"},{"key":"9898_CR21","doi-asserted-by":"crossref","unstructured":"Li C, Luo Z (2011) Detection of shilling attacks in collaborative filtering recommender systems. In: 2011 international conference of soft computing and pattern recognition (SoCPaR). IEEE, pp 190\u2013193","DOI":"10.1109\/SoCPaR.2011.6089138"},{"key":"9898_CR22","doi-asserted-by":"crossref","unstructured":"Massa P, Avesani P (2004) Trust-aware collaborative filtering for recommender systems. In: OTM confederated international conferences\u201d On the Move to Meaningful Internet Systems\u201d. Springer, pp 492\u2013508","DOI":"10.1007\/978-3-540-30468-5_31"},{"key":"9898_CR23","unstructured":"Mobasher B, Burke R, Sandvig JJ (2006) Model-based collaborative filtering as a defense against profile injection attacks. In: AAAI, p 1388"},{"key":"9898_CR24","doi-asserted-by":"crossref","unstructured":"Moradi P, Rezaimehr F, Ahmadian S, Jalili M (2016) A trust-aware recommender algorithm based on users overlapping community structure. In: 2016 Sixteenth international conference on advances in ICT for emerging regions (ICTer). IEEE, pp 162\u2013167","DOI":"10.1109\/ICTER.2016.7829914"},{"key":"9898_CR25","doi-asserted-by":"crossref","unstructured":"Navgaran DZ, Moradi P, Akhlaghian F (2013) Evolutionary based matrix factorization method for collaborative filtering systems. In: 2013 21st Iranian conference on electrical engineering (ICEE). IEEE, pp 1\u20135","DOI":"10.1109\/IranianCEE.2013.6599844"},{"issue":"4","key":"9898_CR26","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1145\/1031114.1031116","volume":"4","author":"M O\u2019Mahony","year":"2004","unstructured":"O\u2019Mahony M, Hurley N, Kushmerick N, Silvestre G (2004) Collaborative recommendation: a robustness analysis. ACM Trans Internet Technol (TOIT) 4(4):344\u2013377","journal-title":"ACM Trans Internet Technol (TOIT)"},{"key":"9898_CR27","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.knosys.2015.03.006","volume":"83","author":"P Pirasteh","year":"2015","unstructured":"Pirasteh P, Hwang D, Jung JJ (2015) Exploiting matrix factorization to asymmetric user similarities in recommendation systems. Knowl Based Syst 83:51\u201357","journal-title":"Knowl Based Syst"},{"key":"9898_CR28","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1016\/j.physa.2014.04.002","volume":"408","author":"M Ramezani","year":"2014","unstructured":"Ramezani M, Moradi P, Akhlaghian F (2014) A pattern mining approach to enhance the accuracy of collaborative filtering in sparse data domains. Phys A Stat Mech Appl 408:72\u201384","journal-title":"Phys A Stat Mech Appl"},{"key":"9898_CR29","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.engappai.2015.08.010","volume":"46","author":"M Ranjbar","year":"2015","unstructured":"Ranjbar M, Moradi P, Azami M, Jalili M (2015) An imputation-based matrix factorization method for improving accuracy of collaborative filtering systems. Eng Appl Artif Intell 46:58\u201366","journal-title":"Eng Appl Artif Intell"},{"key":"9898_CR30","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1016\/j.future.2017.04.003","volume":"78","author":"F Rezaeimehr","year":"2018","unstructured":"Rezaeimehr F, Moradi P, Ahmadian S, Qader NN, Jalili M (2018) Tcars: time-and community-aware recommendation system. Future Gener Comput Syst 78:419\u2013429","journal-title":"Future Gener Comput Syst"},{"key":"9898_CR31","doi-asserted-by":"crossref","unstructured":"Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on world wide web. ACM, pp 285\u2013295","DOI":"10.1145\/371920.372071"},{"issue":"1","key":"9898_CR32","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1007\/s10462-018-9655-x","volume":"53","author":"M Si","year":"2020","unstructured":"Si M, Li Q (2020) Shilling attacks against collaborative recommender systems: a review. Artif Intell Rev 53(1):291\u2013319","journal-title":"Artif Intell Rev"},{"key":"9898_CR33","first-page":"33","volume":"4","author":"T Srikanth","year":"2019","unstructured":"Srikanth T, Shashi M (2019) New metrics for effective detection of shilling attacks in recommender systems. J Inf Eng Electron Bus 4:33\u201342","journal-title":"J Inf Eng Electron Bus"},{"key":"9898_CR34","doi-asserted-by":"crossref","unstructured":"Su X-F, Zeng H-J, Chen Z (2005) Finding group shilling in recommendation system. In: Special interest tracks and posters of the 14th international conference on world wide web. ACM, pp 960\u2013961","DOI":"10.1145\/1062745.1062818"},{"issue":"7","key":"9898_CR35","doi-asserted-by":"publisher","first-page":"949","DOI":"10.1093\/comjnl\/bxy008","volume":"61","author":"C Tong","year":"2018","unstructured":"Tong C, Yin X, Li J, Zhu T, Lv R, Sun L, Rodrigues JJJTCJ (2018) A shilling attack detector based on convolutional neural network for collaborative recommender system in social aware network. The Computer Journal 61(7):949\u2013958","journal-title":"The Computer Journal"},{"key":"9898_CR36","doi-asserted-by":"crossref","unstructured":"Williams C, Mobasher B (2006) Profile injection attack detection for securing collaborative recommender systems, pp 1\u201347","DOI":"10.1007\/s11761-007-0013-0"},{"key":"9898_CR37","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1016\/j.knosys.2016.08.011","volume":"111","author":"Z Yang","year":"2016","unstructured":"Yang Z, Cai Z, Guan X (2016a) Estimating user behavior toward detecting anomalous ratings in rating systems. Knowl Based Syst 111:144\u2013158","journal-title":"Knowl Based Syst"},{"key":"9898_CR38","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.knosys.2016.02.008","volume":"100","author":"Z Yang","year":"2016","unstructured":"Yang Z, Xu L, Cai Z, Xu Z (2016b) Re-scale adaboost for attack detection in collaborative filtering recommender systems. Knowl Based Syst 100:74\u201388","journal-title":"Knowl Based Syst"},{"issue":"6","key":"9898_CR39","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1049\/iet-ifs.2016.0345","volume":"11","author":"L Yang","year":"2017","unstructured":"Yang L, Huang W, Niu X (2017) Defending shilling attacks in recommender systems using soft co-clustering. IET Inf Secur 11(6):319\u2013325","journal-title":"IET Inf Secur"},{"key":"9898_CR40","doi-asserted-by":"crossref","unstructured":"Yang F, Gao M, Yu J, Song Y, Wang X (2018) Detection of shilling attack based on bayesian model and user embedding. In: 2018 IEEE 30th international conference on tools with artificial intelligence (ICTAI). IEEE, pp 639\u2013646","DOI":"10.1109\/ICTAI.2018.00102"},{"issue":"1","key":"9898_CR41","first-page":"226","volume":"7","author":"F Zhang","year":"2012","unstructured":"Zhang F, Zhou QJJC (2012) A meta-learning-based approach for detecting profile injection attacks in collaborative recommender systems. J Comput 7(1):226\u2013234","journal-title":"J Comput"},{"key":"9898_CR42","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.knosys.2014.04.020","volume":"65","author":"F Zhang","year":"2014","unstructured":"Zhang F, Zhou Q (2014) HHT\u2013SVM: an online method for detecting profile injection attacks in collaborative recommender systems. Knowl Based Syst 65:96\u2013105","journal-title":"Knowl Based Syst"},{"key":"9898_CR43","doi-asserted-by":"crossref","unstructured":"Zhang F, Deng Z-J, He Z-M, Lin X-C, Sun L-L (2018a) Detection of shilling attack in collaborative filtering recommender system by PCA and data complexity. In: 2018 international conference on machine learning and cybernetics (ICMLC). IEEE, pp 673\u2013678","DOI":"10.1109\/ICMLC.2018.8526965"},{"key":"9898_CR44","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/j.knosys.2018.02.032","volume":"148","author":"F Zhang","year":"2018","unstructured":"Zhang F, Zhang Z, Zhang P, Wang S (2018b) Ud-hmm: an unsupervised method for shilling attack detection based on hidden markov model and hierarchical clustering. Knowl Based Syst 148:146\u2013166","journal-title":"Knowl Based Syst"},{"issue":"3","key":"9898_CR45","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1049\/iet-ifs.2015.0067","volume":"10","author":"Q Zhou","year":"2016","unstructured":"Zhou Q (2016) Supervised approach for detecting average over popular items attack in collaborative recommender systems. IET Inf Secur 10(3):134\u2013141","journal-title":"IET Inf Secur"},{"issue":"3","key":"9898_CR46","first-page":"687","volume":"9","author":"Q Zhou","year":"2012","unstructured":"Zhou Q, Zhang F (2012) A hybrid unsupervised approach for detecting profile injection attacks in collaborative recommender systems. J Comput 9(3):687\u2013694","journal-title":"J Comput"},{"key":"9898_CR47","doi-asserted-by":"crossref","unstructured":"Zhou W, Koh YS, Wen J, Alam S, Dobbie G (2014) Detection of abnormal profiles on group attacks in recommender systems. In: Proceedings of the 37th international ACM SIGIR conference on research & development in information retrieval. ACM, pp 955\u2013958","DOI":"10.1145\/2600428.2609483"},{"issue":"7","key":"9898_CR48","doi-asserted-by":"publisher","first-page":"e0130968","DOI":"10.1371\/journal.pone.0130968","volume":"10","author":"W Zhou","year":"2015","unstructured":"Zhou W, Wen J, Koh YS, Xiong Q, Gao M, Dobbie G, Alam S (2015) Shilling attacks detection in recommender systems based on target item analysis. PloS one 10(7):e0130968","journal-title":"PloS one"},{"key":"9898_CR49","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1016\/j.neucom.2015.12.137","volume":"210","author":"W Zhou","year":"2016","unstructured":"Zhou W, Wen J, Xiong Q, Gao M, Zeng JJN (2016) SVM-TIA a shilling attack detection method based on svm and target item analysis in recommender systems. Neurocomputing 210:197\u2013205","journal-title":"Neurocomputing"}],"container-title":["Artificial Intelligence Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-020-09898-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10462-020-09898-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-020-09898-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,18]],"date-time":"2022-11-18T05:54:49Z","timestamp":1668750889000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10462-020-09898-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,14]]},"references-count":49,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2021,3]]}},"alternative-id":["9898"],"URL":"https:\/\/doi.org\/10.1007\/s10462-020-09898-3","relation":{},"ISSN":["0269-2821","1573-7462"],"issn-type":[{"value":"0269-2821","type":"print"},{"value":"1573-7462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,14]]},"assertion":[{"value":"14 September 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}