A shilling attack detector based on convolutional neural network for collaborative recommender system in social aware network

One of the most fundamental tasks in the socially aware network (SAN) paradigm is to explore the attributes and behavior of users, which helps to design more suitable and efficient protocols. Particularly, detection of shilling attackers by mining users' behavior is a frequently discussed topic in many social scenes like recommender systems based on collaborative filtering. As the performances of collaborative filtering are entirely based on ratings provided by users, they are vulnerable to shilling attacks which perform injection of biased profiles into rating databases to alter the systems. Current shilling attack detection methods detect spam users through artificially designed features, which are neither robust nor efficient enough. This paper illustrates a novel convolutional neural network-based method named CNN-SAD, which applies transformed network structure to exploit deep-level features from users rating profiles. Since the achieved deep-level features elaborate users rating more precisely than artificially designed features, CNN-SAD can detect shilling attacks more efficiently. According to the experimental results, the proposed method is capable of detecting the vast majority of obfuscated attacks precisely and outperforms other state-of-the-art algorithms, which contributes to applications and security in SAN.