  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.
