A Comparative Evaluation of Profile Injection Attacks Anjani Kumar Verma and Veer Sain Dixit Abstract In recent years, the research on shilling attacks has been greatly improved. However, some serious problem in hand such as attack model dependency and high computational cost. Such recommender system also provides an impressive way to overcome information overload problem. In order to preserve the trust of rec- ommender system, it is required to identify and remove the fictitious profiles from the system. Here, we have used machine learning classifiers to detect the attacker’s profiles. A new model is proposed that outperforms in most of the cases. Keywords Recommender systems · Collaborative filtering · Shilling attacks MAE · RMSE 1 Introduction In recent years, the effective profile injection or shilling attacks are more emphasized toward the insertion of bogus user profiles into the system database in order to manip- ulate the recommendation output, which is actually used to promote or demote the predicted ratings for a particular product. In many e-commerce websites, the recom- mender systems are widely deployed to provide user-purchasing suggestion. With rapid change in technology, most recommender systems adopted collaborative filter- ing. However, with the open nature of collaborative filtering recommender systems, it suffers from significant vulnerabilities from being attacked by malicious raters, who inject profiles consisting of biased ratings. Hence, they may have an effective impact on produced predictions. The basic function of a recommender system is to sense the consumer’s feedback (which can be present in many forms like implicit, explicit, etc.) and understand user’s interests to benefit consumers and e-business owners. A. K. Verma (B) Department of Computer Science, University of Delhi, New Delhi, India e-mail: anjaniverma29@gmail.com V. S. Dixit Department of Computer Science, ARSD College, University of Delhi, New Delhi, India e-mail: veersaindixit@rediffmail.com © Springer Nature Singapore Pte Ltd. 2019 M. L. Kolhe et al. (eds.), Advances in Data and Information Sciences, Lecture Notes in Networks and Systems 39, https://doi.org/10.1007/978-981-13-0277-0_4 43