Fake detection in imbalance dataset by Semi-supervised learning with GAN

As social media grows faster, harassment becomes more prevalent which leads to considered fake detection a fascinating field among researchers. The graph nature of data with the large number of nodes caused different obstacles including a considerable amount of unrelated features in matrices as high dispersion and imbalance classes in the dataset. To deal with these issues Auto-encoders and a combination of semi-supervised learning and the GAN algorithm which is called SGAN were used. This paper is deploying a smaller number of labels and applying SGAN as a classifier. The result of this test showed that the accuracy had reached 91\% in detecting fake accounts using only 100 labeled samples.

Detecting fake accounts through Generative Adversarial Network in online social media

Nowadays, online social media has become an inseparable part of human life, also this phenomenon is being used by individuals to send messages and share files via videos and images. Twitter, Instagram, and Facebook are well-known samples of these networks. One of the main challenges of privacy for users in these networks is anomalies in security. Anomalies in online social networks can be attributed to illegal behavior, such deviance is done by malicious people like account forgers, online fraudsters, etc. This paper proposed a new method to identify fake user accounts by calculating the similarity measures among users, applying the Generative Adversarial Network (GAN) algorithm over the Twitter dataset. The results of the proposed method showed, accuracy was able to reach 98.1% for classifying and detecting fake user accounts.