| Title | Efficient Detection of Shilling’s Attacks in Collaborative Filtering Recommendation Systems Using Deep Learning Models |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Ebrahimian, Mahsa, Kashef, Rasha |
| Conference Name | 2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) |
| Date Published | dec |
| Keywords | convolutional neural networks, Convolutional Neural Networks (CNN), Data models, Deep Learning, feature extraction, human factors, Motion pictures, pubcrawl, recommender systems, Recurrent neural networks, Recurrent Neural Networks (RNN), Resiliency, Scalability, shilling attacks, Size measurement |
| Abstract | Recommendation systems, especially collaborative filtering recommenders, are vulnerable to shilling attacks as some profit-driven users may inject fake profiles into the system to alter recommendation outputs. Current shilling attack detection methods are mostly based on feature extraction techniques. The hand-designed features can confine the model to specific domains or datasets while deep learning techniques enable us to derive deeper level features, enhance detection performance, and generalize the solution on various datasets and domains. This paper illustrates the application of two deep learning methods to detect shilling attacks. We conducted experiments on the MovieLens 100K and Netflix Dataset with different levels of attacks and types. Experimental results show that deep learning models can achieve an accuracy of up to 99%. |
| DOI | 10.1109/IEEM45057.2020.9309965 |
| Citation Key | ebrahimian_efficient_2020 |
Efficient Detection of Shilling’s Attacks in Collaborative Filtering Recommendation Systems Using Deep Learning Models