Method for Detecting Manipulation Attacks on Recommender Systems with Collaborative Filtering

1. Recommendation Engine Market Size, Share & Trends Analysis Report by Type (Collaborative Filtering, Hybrid Recommendation), by Deployment, by Application, by Organization, by End-use, by Region, and Segment Forecasts, 2021–2028. https://www.grandviewresearch.com/industry-analysis/recommendation-engine-market-report. Cited December 10, 2022.

2. Wang, J. and Tang, Q., Recommender systems and their security concerns, 2015. https://hdl.handle.net/10993/30100.

3. Zegzhda, D., Lavrova, D., Pavlenko, E., and Shtyrkina, A., Cyber attack prevention based on evolutionary cybernetics approach, Symmetry, 2020, vol. 12, no. 11, p. 1931. https://doi.org/10.3390/sym12111931

   Article  ADS  Google Scholar 

4. Kalinin, M.O., Zubkov, E.A., Suprun, A.F., and Pechenkin, A.I., Prevention of attacks on dynamic routing in self-organizing adhoc networks using swarm intelligence, Autom. Control Comput. Sci., 2018, vol. 52, no. 8, pp. 977–983. https://doi.org/10.3103/s0146411618080163

   Article  Google Scholar 

5. Pavlenko, E., Zegzhda, D., and Poltavtseva, M., Ensuring the sustainability of cyberphysical systems based on dynamic reconfiguration, 2019 IEEE Int. Conf. on Industrial Cyber Physical Systems (ICPS), Taipei, 2019, IEEE, 2019, pp. 60–64. https://doi.org/10.1109/icphys.2019.8780193

6. Lam, S.K. and Riedl, J., Shilling recommender systems for fun and profit, Proc. 13th Int. Conf. on World Wide Web, New York, 2004, New York: Association for Computing Machinery, 2004, pp. 393–402.

7. Sundar, A.P., Li, F., Zou, X., Gao, T., and Russomanno, E.D., Understanding shilling attacks and their detection traits: A comprehensive survey, IEEE Access, 2020, vol. 8, pp. 171703–171715. https://doi.org/10.1109/access.2020.3022962

   Article  Google Scholar 

8. Mehta, B. and Nejdl, W., Unsupervised strategies for shilling detection and robust collaborative filtering, User Model. User-Adapted Interaction, 2008, vol. 19, nos. 1–2, pp. 65–97. https://doi.org/10.1007/s11257-008-9050-4

   Article  Google Scholar 

9. Bryan, K., O’Mahony, M., and Cunningham, P., Unsupervised retrieval of attack profiles in collaborative recommender systems, Proc. 2008 ACM Conf. on Recommender Systems, Lausanne, 2008, New York: Association for Computing Machinery, 2008, pp. 155–162. https://doi.org/10.1145/1454008.1454034

10. Bhaumik, R., Mobasher, B., and Burke, R., A clustering approach to unsupervised attack detection in collaborative recommender systems, Proc. Int. Conf. on Data Science (ICDATA).—The Steering Committee of the World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp), Las Veas, 2011, Stahlbock, R., Ed., CSREA Press, 2011, p. 1.

11. Bilge, A., Ozdemir, Z., and Polat, H., A novel shilling attack detection method, Procedia Comput. Sci., 2014, vol. 31, pp. 165–174. https://doi.org/10.1016/j.procs.2014.05.257

    Article  Google Scholar 

12. Chung, Ch.-Ya., Hsu, P.-Yu., and Huang, Sh.-H., βP: A novel approach to filter out malicious rating profiles from recommender systems, Decision Support Syst., 2013, vol. 55, no. 1, pp. 314–325. https://doi.org/10.1016/j.dss.2013.01.020

    Article  Google Scholar 

13. Zhang, F., Zhang, Z., Zhang, P., and Wang, S., UD-HMM: An unsupervised method for shilling attack detection based on hidden Markov model and hierarchical clustering, Knowl.-Based Syst., 2018, vol. 148, pp. 146–166. https://doi.org/10.1016/j.knosys.2018.02.032

    Article  Google Scholar 

14. Zhang, F., Deng, Z.-J., He, Zh.-M., Lin, X.-Ch., and Sun, L.-L., Detection of shilling attack in collaborative filtering recommender system by PCA and data complexity, 2018 Int. Conf. on Machine Learning and Cybernetics (ICMLC), Chengdu, China, 2018, IEEE, 2018, pp. 673–678. https://doi.org/10.1109/icmlc.2018.8526965

15. Maxwell Harper, F. and Konstan, J.A., The MovieLens datasets: History and context, ACM Trans. Interactive Intell. Syst. (TiiS), 2015, vol. 5, no. 4, p. 19. https://doi.org/10.1145/2827872

    Article  Google Scholar 

Download references