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. A single Support Vector Machine (SVM) approach for the detection of profile injection attacks, however, suffers from low precision. With this problem in mind, in this paper we propose a meta-learning-based approach to detect such attacks. In particular, we propose an algorithm to create the diverse base-level training sets through flexible combination of various attack types. Combining the created training sets with SVM, we construct the base-level and meta-level classifiers. Based on these classifiers, we present a meta-learning-based detection algorithm which uses the meta-classifier to integrate the outputs of the base-classifiers and generates the final results of detection. The diversities among the base-classifiers effectively reduce the correlation of the misclassifications and improve the predictive capability of the meta-level. We conduct comparative experiments with a single SVM and the voting-based ensemble method on different-scale MovieLens datasets. The experimental results show that the proposed approach can effectively improve the precision under the condition of holding a high recall
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.
Recommendation attack attempts to bias the recommendation results of collaborative recommender systems by injecting malicious ratings into the rating database. A lot of methods have been proposed for detecting such attacks. Among these works, the deep learning-based detection methods get rid of the dependence on hand-designed features of recommendation attack besides having excellent detection performance. However, most of them optimize the key hyperparameters by manual analysis which relies too much on domain experts and their experience. To address this issue, in this paper we propose an approach based on the Harris Hawks Optimization (HHO) algorithm to improve the deep learning-based detection methods. Being different from the original detection methods which optimize the key hyperparameters manually, the improved deep learning-based detection methods can optimize the key hyperparameters automatically. We first convert the key hyperparameters of discrete type to continuous type according to the uniform distribution theory to expand the application scope of HHO algorithm. Then, we use the detection stability as an early stop condition to reduce the optimization iterations to improve the HHO algorithm. After that, we use the improved HHO algorithm to automatically optimize the key hyperparameters for the deep learning-based detection methods. Finally, we use the optimized key hyperparameters to train the deep learning-based detection methods to generate classifiers for detecting the recommendation attack. The experiments conducted on two benchmark datasets illustrate that the improved deep learning-based detection methods have effective performance.
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