论文标题
通过在线社交媒体中的生成对抗网络检测假帐户
Detecting fake accounts through Generative Adversarial Network in online social media
论文作者
论文摘要
在线社交媒体是人类生活不可或缺的一部分，促进消息传递，信息共享和机密通信，同时保留隐私。 Twitter，Instagram和Facebook等平台体现了这一现象。但是，用户由于网络异常而面临的挑战，通常是由于恶意活动（例如身份盗窃）造成财务收益或危害而引起的。本文提出了一种新的方法，使用用户相似性度量和生成对抗网络（GAN）算法，以在Twitter数据集中识别假用户帐户。尽管问题的复杂性，该方法在分类和检测假帐户时仍达到80 \％的AUC率。值得注意的是，该研究以先前的研究为基础，强调了对在线社交网络中异常检测不断发展的景观的进步和见解。
Online social media is integral to human life, facilitating messaging, information sharing, and confidential communication while preserving privacy. Platforms like Twitter, Instagram, and Facebook exemplify this phenomenon. However, users face challenges due to network anomalies, often stemming from malicious activities such as identity theft for financial gain or harm. This paper proposes a novel method using user similarity measures and the Generative Adversarial Network (GAN) algorithm to identify fake user accounts in the Twitter dataset. Despite the problem's complexity, the method achieves an AUC rate of 80\% in classifying and detecting fake accounts. Notably, the study builds on previous research, highlighting advancements and insights into the evolving landscape of anomaly detection in online social networks.
