Efficient Detection of Shillings Attacks in Collaborative Filtering Recommendation Systems using Hybrid Deep Learning: A CNN-based Model and Architecture

Abstract

<p>Recommendation systems are widely used in various areas to personalize recommendations to users. However, they are vulnerable to shilling attacks in which malicious users try to promote their products or diminish their competitors. Therefore, detecting shilling attacks can significantly improve the quality of recommender systems. With the increasing complexity of attacks and changes in attackers' behaviour, more advanced approaches are required to find the hidden patterns in data. This thesis proposes a CNN-based hybrid model and architecture that integrates self-learning and flexible aspects of CNN with other supervised learning methods to enhance shillings attacks detection on collaborative filtering recommendation systems. We also introduce a new metric, the F-compatible score, to measure the compatibility ratio of merging the CNN with any other classifier in a given aggregate. This measure helps in monitoring the enhancement level of the detection results of shilling attacks. Two different approaches are proposed in this thesis, user-based, and item-based. The experimental analysis used three benchmark datasets: the Movie-Lens 100K, Netflix, and Movie-Lens 1M. A comprehensive comparative analysis is also completed to assess the performance of both individual-based and hybrid-based detection models. Experimental results show that the proposed hybrid models performed well on most attack profiles and reached an F1-score of up to 99%. We concluded that the superiority of hybrid models over individual models depends on the sparsity level of data and the divergence of results.</p>