Scalable Anomaly Detection with Machine Learning: Techniques for Managing High-Dimensional Data Streams

Abstract

The increase in big data values from industries, especially in Analytics, Risk, and Management Information systems, offers a great catchment and, at the same time, books with a lot of potential hurdles. This paper provides the conceptual basis for combining unsupervised and deep learning for real-time anomaly detection in high feature-space trajectories and offers practical applications for several industries. Autoencoders, Isolation Forests, and RNNs are tested for their performance and compared with the PCA in finance, manufacturing, healthcare, and cybersecurity applications. Other benefits are measured in specifics, such as cutting fraud detection mistakes by 25% and increasing the effectiveness of predictive maintenance. Further, the examined study focuses on problems like big data, real-time streaming data, distributed computing, edge computing, and incremental learning. Some key contributions are the scalable methods for IoT system-driven scenarios and the possibility of quickly recognizing anomalies with low latency. The paper also outlines emerging aspects of data quality, model interpretability, data privacy, and possible strategies such as Explainable AI and data masking. Examples from fraud detection and equipment failure prediction further explain the various opportunities these models offer for making decisions more effectively in complex and evolving environments.