Palanisamy Sundar, AgnidevenLi, FengZou, XukaiGao, TianchongRussomanno, Evan D.2022-02-082022-02-082020-09Sundar, A. P., Li, F., Zou, X., Gao, T., & Russomanno, E. D. (2020). Understanding Shilling Attacks and Their Detection Traits: A Comprehensive Survey. IEEE Access, 8, 171703–171715. https://doi.org/10.1109/ACCESS.2020.3022962https://hdl.handle.net/1805/27728The internet is the home for huge volumes of useful data that is constantly being created making it difficult for users to find information relevant to them. Recommendation System is a special type of information filtering system adapted by online vendors to provide recommendations to their customers based on their requirements. Collaborative filtering is one of the most widely used recommendation systems; unfortunately, it is prone to shilling/profile injection attacks. Such attacks alter the recommendation process to promote or demote a particular product. Over the years, multiple attack models and detection techniques have been developed to mitigate the problem. This paper aims to be a comprehensive survey of the shilling attack models, detection attributes, and detection algorithms. Additionally, we unravel and classify the intrinsic traits of the injected profiles that are exploited by the detection algorithms, which has not been explored in previous works. We also briefly discuss recent works in the development of robust algorithms that alleviate the impact of shilling attacks, attacks on multi-criteria systems, and intrinsic feedback based collaborative filtering methods.enPublisher PolicyAttribution 4.0 Internationalcollaborative filteringdetection traits and algorithmsprofile injection attacksArticle