AnoGAN for Tabular Data: A Novel Approach to Anomaly Detection
It addresses anomaly detection for applications like cybersecurity and healthcare, but appears incremental as it extends an existing method to a new domain.
This research tackles anomaly detection in tabular data by adapting AnoGAN from image domains, resulting in promising advancements for detecting previously undetectable anomalies.
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.