Hybrid gated recurrent unit and convolutional neural network-based deep learning mechanism for efficient shilling attack detection in social networks

The degree of openness of the socially aware recommendation systems and the possibility of the attackers injecting vast numbers of fake profiles biases the prediction of the system. Most classical shilling attack discovery mechanisms rely on artificially derived features, generally determined from user-generated data. Moreover, these baseline approaches cannot comprehensively capture the fine-grained relation between users and objects, thereby lagging in detection accuracy. This work introduces a Hybrid deep learning mechanism to address the Shilling Attack discovery with improved accuracy. The proposed mechanism highly enhances the detection accuracy by determining significant feature vectors managed by recurrent gated units and other high dimensional attribute vectors attained by using CNN. The GRU component is incorporated for the dimensionality transition process for existing weight classification data to learn the inherent significant features. The CNN mechanism is mainly used for analyzing the conditions of spatial-temporal data and transforming the data into a dormant feature vector. The experimental analysis of our proposed method is conducted using the datasets of Amazon, Netflix, and Movielens to demonstrate its predominance by enhancing accuracy, precision, and F-score.