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.

本文提出了一种使用 GAN 算法通过 Twitter 数据集计算用户之间的相似度，以识别和检测假用户账户的新方法，并实验结果表明，该方法的准确率能够达到 98.1％。

通过生成对抗网络在在线社交媒体中检测虚假账户

Detecting fake accounts through Generative Adversarial Network in online social media

In this paper, we study the problem of early detection of fake user accounts
on social networks based solely on their network connectivity with other users.
Removing such accounts is a core task for maintaining the integrity of social
networks, and early detection helps to reduce the harm that such accounts
inflict. However, new fake accounts are notoriously difficult to detect via
graph-based algorithms, as their small number of connections are unlikely to
reflect a significant structural difference from those of new real accounts. We
present the SybilEdge algorithm, which determines whether a new user is a fake
account (`sybil') by aggregating over (I) her choices of friend request targets
and (II) these targets' respective responses. SybilEdge performs this
aggregation giving more weight to a user's choices of targets to the extent
that these targets are preferred by other fakes versus real users, and also to
the extent that these targets respond differently to fakes versus real users.
We show that SybilEdge rapidly detects new fake users at scale on the Facebook
network and outperforms state-of-the-art algorithms. We also show that
SybilEdge is robust to label noise in the training data, to different
prevalences of fake accounts in the network, and to several different ways
fakes can select targets for their friend requests. To our knowledge, this is
the first time a graph-based algorithm has been shown to achieve high
performance (AUC>0.9) on new users who have only sent a small number of friend
requests.

本文介绍了基于社交网络的用户连接情况的早期检测假用户帐户的问题，以及引入了一种名为 SybilEdge 算法的解决方案，并展示了其对 Facebook 网络的高性能识别表现。