An Empirical Study on Anomaly Detection and Optimization in ICH Information Management Systems

Anomaly detection plays a critical role in fields such as finance, healthcare, and industrial monitoring, where identifying irregular patterns can prevent system failures and enhance security. Traditional methods, however, often struggle with handling multimodal data and adapting to dynamic environments where data distributions change over time. Additionally, many models face difficulties in balancing false positive and false negative rates, particularly when dealing with imbalanced or noisy datasets. This paper proposes a novel approach, MDA-GAN (Multimodal Dynamic Autoencoder with Generative Adversarial Networks), to address these challenges. The method integrates deep autoencoders for feature extraction, utilizes GANs to generate high-quality normal samples, and incorporates dynamic threshold adjustment to enhance adaptability to evolving data patterns. By combining these components, MDA-GAN improves anomaly detection accuracy, robustness, and adaptability, particularly in complex multimodal data environments.