  Anomaly detection, a critical facet in data analysis, involves identifying
patterns that deviate from expected behavior. This research addresses the
complexities inherent in anomaly detection, exploring challenges and adapting
to sophisticated malicious activities. With applications spanning
cybersecurity, healthcare, finance, and surveillance, anomalies often signify
critical information or potential threats. Inspired by the success of Anomaly
Generative Adversarial Network (AnoGAN) in image domains, our research extends
its principles to tabular data. Our contributions include adapting AnoGAN's
principles to a new domain and promising advancements in detecting previously
undetectable anomalies. This paper delves into the multifaceted nature of
anomaly detection, considering the dynamic evolution of normal behavior,
context-dependent anomaly definitions, and data-related challenges like noise
and imbalances.
