Detecting Malicious Social Robots with Generative Adversarial Networks

Abstract

Malicious social robots, which are disseminators of malicious information on social networks, seriously affect information security and network environments. The detection of malicious social robots is a hot topic and a significant concern for researchers. A method based on classification has been widely used for social robot detection. However, this method of classification is limited by an unbalanced data set in which legitimate, negative samples outnumber malicious robots (positive samples), which leads to unsatisfactory detection results. This paper proposes the use of generative adversarial networks (GANs) to extend the unbalanced data sets before training classifiers to improve the detection of social robots. Five popular oversampling algorithms were compared in the experiments, and the effects of imbalance degree and the expansion ratio of the original data on oversampling were studied. The experimental results showed that the proposed method achieved better detection performance compared with other algorithms in terms of the F1 measure. The GAN method also performed well when the imbalance degree was smaller than 15%.

Generative Adversarial Networks for single image with high quality image

“…This method skips the estimation of intermediate parameters and directly performs end-to-end image dehazing, which can generate more realistic and natural dehazing images. Wu et al use generative adversarial network to detect malicious social robots [18]. This method expanded the unbalanced data sets by generative adversarial networks, improved the detection of social robots, and achieved better detection results.…”