ShAD-SEF: An Efficient Model for Shilling Attack Detection using Stacking Ensemble Framework in Recommender Systems

Recommender Systems helps users to find suitable products from massively available data on the internet. The most broadly applied recommendation method is collaborative filtering, which can also be subject to shilling attacks. Profile injection occurs when malicious users insert a few bogus profiles into the user-item rating database, which alters the result of the recommendation. In this paper, the shilling attack is simulated: a Random attack, segment attack, average attack, and bandwagon attack on the movie lens dataset, focusing on users with similar interests. To build trust in the system, fake profiles must be detected. Accuracy, attack size, and filler size computations were done for each model. Several machine learning algorithms are in use to classify these fake and original profiles. Here, four Machine Learning algorithms are compared and the most efficient models are KNN, random forest, and xgboost. To get more accuracy, the ensemble model used logistic regression as a meta classifier which is more accurate than individual machine learning algorithms. Our proposed model, which is stacking an ensemble model using logistic regression as a meta-classifier, will give the best accuracy in any case.