Anomaly Detection Using Machine Learning: DBSCAN, Autoencoder and GMM Approaches

Abstract: This research proposes a novel approach to anomaly detection by combining autoencoders and Gaussian Mixture Models (GMMs). Autoencoders are utilized for dimensionality reduction and feature extraction, while GMMs modelthedistributionofthe encoded data. Thehybridmethod aims to combine the advantages of both techniques to improve anomaly detection accuracy across various domains. Experiments conducted on multiple datasets show how effective the suggested strategy is in comparison to both conventional and modern approaches. The results show improved precision, recall, and F1-scores, highlighting the promise for reliable anomaly identification in complicated data settings with this integrated technique