Ensemble detection model for profile injection attacks in collaborative recommender systems based on BP neural network

The existing supervised approaches suffer from low precision when detecting profile injection attacks. To solve this problem, the authors propose an ensemble detection model by introducing back propogation (BP) neural network and ensemble learning technique. Firstly, through combination of various attack types, they create base training sets which include various samples of attack profiles and have great diversities with each other. Secondly, they use the created base training sets to train BP neural networks to generate diverse base classifiers. Finally, they select parts of the base classifiers which have the highest precision on the validation dataset and integrate them using voting strategy. Uncorrelated misclassifications generated by each base classifier can be successfully corrected by the ensemble learning. The experimental results on two different scale of the real datasets MovieLens and Netflix show that the proposed model can effectively improve the precision under the condition of holding a high recall.