AI-Powered Anomaly Detection in Distributed Data Engineering Systems

Abstract

In the era of big data and cloud computing, distributed data engineering systems face significant challenges related to anomaly detection, which can impact system reliability and data integrity. Traditional methods often fall short in identifying anomalies in complex, high-dimensional data due to their static and heuristic-based nature. This paper explores an AI-powered approach to anomaly detection using advanced machine learning techniques, specifically focusing on deep learning models and ensemble methods. By leveraging neural networks, such as autoencoders and recurrent neural networks (RNNs), along with ensemble techniques like Isolation Forest and OneClass SVM, the proposed approach enhances the detection of anomalies in distributed data engineering environments. The study evaluates the effectiveness of these AI-powered models in detecting various types of anomalies, including outliers and system faults, through extensive experiments conducted on benchmark datasets. The results demonstrate significant improvements in detection accuracy and reduction in false positives compared to traditional methods. This paper also discusses the practical implications of integrating AI-powered anomaly detection systems into distributed architectures, highlighting their potential to enhance system reliability and operational efficiency.