AnoGAN for Tabular Data: A Novel Approach to Anomaly Detection

arXiv:2405.03075v1 Announce Type: new
Abstract: 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 …

abstract analysis anomaly anomaly detection applications arxiv behavior challenges complexities cs.lg cybersecurity data data analysis detection facet finance healthcare novel patterns research surveillance tabular tabular data type