Recent research has shown the significant vulnerabilities of collaborative recommender systems in the face of profile injection attacks, in which malicious users insert fake profiles into the rating database in order to bias the system's output. To reduce this risk, a number of approaches have been proposed to detect such attacks. Although the existing detection approaches can detect the standard type of these attacks effectively, they perform badly when detecting the recently proposed obfuscated type of these attacks, for example, average over popular items (AoP) attack. With this problem in mind, in this study the author propose a supervised approach to detect such attack. First, he uses the theory of term frequency inverse document frequency (TFIDF) to extract the features of AoP attack. Second, he uses the training set to train support vector machine (SVM) to generate a SVM‐based classifier. Finally, he uses the generated classifier to detect the AoP attack. The experimental results on MovieLens dataset show that the proposed approach can detect AoP attack with high recall and precision.

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.