METHOD FOR DETECTING SHILLING ATTACKS BASED ON IMPLICIT FEEDBACK IN RECOMMENDER SYSTEMS

Keywords: e-commerce; recommendation system; temporal rules; shilling attack; feedback

Abstract

The problem of identifying shilling attacks, which are aimed at forming false ratings of objects in the recommender system, is considered. The purpose of such attacks is to include in the recommended list of items the goods specified by the attacking user. The recommendations obtained as a result of the attack will not correspond to customers' real preferences, which can lead to distrust of the recommender system and a drop in sales. The existing methods for detecting shilling attacks use explicit feedback from the user and are focused primarily on building patterns that describe the key characteristics of the attack. However, such patterns only partially take into account the dynamics of user interests. A method for detecting shilling attacks using implicit feedback is proposed by comparing the temporal description of user selection processes and ratings. Models of such processes are formed using a set of weighted temporal rules that define the relationship in time between the moments when users select a given object. The method uses time-ordered input data. The method includes the stages of forming sets of weighted temporal rules for describing sales processes and creating ratings, calculating a set of ratings for these processes, and forming attack indicators based on a comparison of the ratings obtained. The resulting signs make it possible to distinguish between nuke and push attacks. The method is designed to identify discrepancies in the dynamics of purchases and ratings, even in the absence of rating values at certain time intervals. The technique makes it possible to identify an approach to masking an attack based on a comparison of the rating values and the received attack indicators. When applied iteratively, the method allows to refine the list of profiles of potential attackers. The technique can be used in conjunction with pattern-oriented approaches to identifying shilling attacks

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Author Biographies

Oksana Chala, Kharkiv National University of Radio Electronics

Department of Information Control Systems

Lyudmyla Novikova, V. N. Karazin Kharkiv National University

Department of International Relations

International Information and Security

Larysa Chernyshova, V. N. Karazin Kharkiv National University

Department of International Economics

Angelika Kalnitskaya, Kharkiv National University of Radio Electronics

Department of Information Control Systems

References

Aggarwal, C. (2016). Recommender Systems. Springer, 498. doi: https://doi.org/10.1007/978-3-319-29659-3

Hallinan, B., Striphas, T. (2014). Recommended for you: The Netflix Prize and the production of algorithmic culture. New Media & Society, 18 (1), 117–137. doi: https://doi.org/10.1177/1461444814538646

Adomavicius, G., Bockstedt, J., Curley, S., Zhang, J. (2014). De-Biasing User Preference Ratings in Recommender Systems. Proceedings of the Joint Workshop on Interfaces and Human Decision Making for Recommender Systems co-located with ACM Conference on Recommender Systems, 2–9. Available at: http://ceur-ws.org/Vol-1253/paper1.pdf

Gunes, I., Kaleli, C., Bilge, A., Polat, H. (2012). Shilling attacks against recommender systems: a comprehensive survey. Artificial Intelligence Review, 42 (4), 767–799. doi: https://doi.org/10.1007/s10462-012-9364-9

Adomavicius, G., Bockstedt, J. C., Curley, S. P., Zhang, J. (2013). Do Recommender Systems Manipulate Consumer Preferences? A Study of Anchoring Effects. Information Systems Research, 24 (4), 956–975. doi: https://doi.org/10.1287/isre.2013.0497

Mobasher, B., Burke, R., Bhaumik, R., Williams, C. (2007). Toward trustworthy recommender systems: an analysis of attack models and algorithm robustness. ACM Transactions on Internet Technology, 7 (4), 23. doi: https://doi.org/10.1145/1278366.1278372

Wang, Y., Qian, L., Li, F., Zhang, L. (2018). A Comparative Study on Shilling Detection Methods for Trustworthy Recommendations. Journal of Systems Science and Systems Engineering, 27 (4), 458–478. doi: https://doi.org/10.1007/s11518-018-5374-8

Patel, K., Thakkar, A., Shah, C., Makvana, K. (2016). A State of Art Survey on Shilling Attack in Collaborative Filtering Based Recommendation System. Smart Innovation, Systems and Technologies, 377–385. doi: https://doi.org/10.1007/978-3-319-30933-0_38

Zhou, W., Wen, J., Gao, M., Ren, H., Li, P. (2015). Abnormal Profiles Detection Based on Time Series and Target Item Analysis for Recommender Systems. Mathematical Problems in Engineering, 2015, 1–9. doi: https://doi.org/10.1155/2015/490261

Wang, Y., Qian, L., Li, F., Zhang, L. (2018). A Comparative Study on Shilling Detection Methods for Trustworthy Recommendations. Journal of Systems Science and Systems Engineering, 27 (4), 458–478. doi: https://doi.org/10.1007/s11518-018-5374-8

Gao, M., Yuan, Q., Ling, B., Xiong, Q. (2014). Detection of Abnormal Item Based on Time Intervals for Recommender Systems. The Scientific World Journal, 2014, 1–8. doi: https://doi.org/10.1155/2014/845897

Gao, M., Tian, R., Wen, J., Xiong, Q., Ling, B., Yang, L. (2015). Item Anomaly Detection Based on Dynamic Partition for Time Series in Recommender Systems. PLOS ONE, 10 (8), e0135155. doi: https://doi.org/10.1371/journal.pone.0135155

Chala, O., Novikova, L., Chernyshova, L. (2019). Method for detecting shilling attacks in e-commerce systems using weighted temporal rules. EUREKA: Physics and Engineering, 5, 29–36. doi: https://doi.org/10.21303/2461-4262.2019.00983

Levykin, V., Chala, O. (2018). Method of determining weights of temporal rules in Markov logic network for building knowledge base in information control systems. EUREKA: Physics and Engineering, 5, 3–10. doi: https://doi.org/10.21303/2461-4262.2018.00713

Chalyi, S., Leshchynskyi, V., Leshchynska, I. (2019). Method of forming recommendations using temporal constraints in a situation of cyclic cold start of the recommender system. EUREKA: Physics and Engineering, 4, 34–40. doi: https://doi.org/10.21303/2461-4262.2019.00952

Chalyi, S., Pribylnova, I. (2019). The method of constructing recommendations online on the temporal dynamics of user interests using multilayer graph. EUREKA: Physics and Engineering, 3, 13–19. doi: https://doi.org/10.21303/2461-4262.2019.00894

Sergii, C., Ihor, L., Aleksandr, P., Ievgen, B. (2018). Causality-based model checking in business process management tasks. 2018 IEEE 9th International Conference on Dependable Systems, Services and Technologies (DESSERT). doi: https://doi.org/10.1109/dessert.2018.8409176

Zajac, Z. (2017). Goodbooks-10k: a new dataset for book recommendations. FastML. Available at: http://fastml.com/goodbooks-10k-a-new-dataset-for-book-recommendations/


Abstract views: 61
PDF Downloads: 32
Published
2020-09-30
How to Cite
ChalaO., NovikovaL., ChernyshovaL., & KalnitskayaA. (2020). METHOD FOR DETECTING SHILLING ATTACKS BASED ON IMPLICIT FEEDBACK IN RECOMMENDER SYSTEMS. EUREKA: Physics and Engineering, (5), 21-30. https://doi.org/10.21303/2461-4262.2020.001394
Section
Computer Sciences