Innovative Approaches to Anomaly Detection in Large-Scale Systems

Abstract

This paper explores innovative approaches to anomaly detection in large-scale systems, addressing the limitations of traditional methods such as scalability issues and high false positive rates. Anomaly detection is critical in various domains including financial networks, healthcare, and industrial operations, where early detection of anomalies can prevent significant adverse outcomes. Traditional statistical and machine learning methods often struggle with high-dimensional data and dynamic environments. This study investigates modern techniques like deep learning and ensemble methods that leverage large datasets and complex models to enhance detection accuracy. Specifically, the paper examines the use of autoencoders, Long Short-Term Memory (LSTM) networks, and Generative Adversarial Networks (GANs) for their ability to handle complex, high-dimensional data and adapt to evolving patterns. Ensemble methods, such as Isolation Forests and multiple autoencoders, are also evaluated for their robustness and efficiency. Through empirical analysis and case studies, the study demonstrates that these innovative approaches significantly improve anomaly detection performance, offering valuable insights and practical solutions for maintaining the integrity and performance of large-scale systems.