面向表格数据的AnoGAN：一种新颖的异常检测方法
AnoGAN for Tabular Data: A Novel Approach to Anomaly Detection
AI总结：
本文将图像领域的AnoGAN原理扩展到表格数据，提出一种新颖的异常检测方法，以应对正常行为动态演变、上下文相关定义及噪声和不平衡等挑战，并展示了检测此前无法检测异常的前景。
摘要：
异常检测是数据分析中的一个关键方面，涉及识别偏离预期行为的模式。本研究针对异常检测中固有的复杂性，探索相关挑战并适应复杂的恶意活动。其应用涵盖网络安全、医疗保健、金融和监控等领域，异常往往意味着关键信息或潜在威胁。受异常生成对抗网络（AnoGAN）在图像领域取得成功的启发，我们的研究将其原理扩展到表格数据。我们的贡献包括将AnoGAN的原理适配到一个新领域，并在检测此前无法检测的异常方面取得了有前景的进展。本文深入探讨了异常检测的多面性，考虑了正常行为的动态演变、依赖上下文的异常定义以及与噪声和不平衡等数据相关的挑战。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
