AnoGAN for Tabular Data: A Novel Approach to Anomaly Detection

TL;DR

This paper adapts AnoGAN, a generative adversarial network, for anomaly detection in tabular data, addressing domain-specific challenges and demonstrating potential for improved detection of complex anomalies.

Contribution

It introduces a novel application of AnoGAN to tabular data, extending its capabilities beyond image domains for enhanced anomaly detection.

Findings

Effective detection of complex anomalies in tabular data

Addresses challenges like noise and data imbalance

Shows promise in identifying previously undetectable anomalies

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 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.