Student Name

Arman BariFollow


Master of Science in Data Science


Department of Computer Science

Faculty/ School

School of Mathematics and Computer Science (SMCS)

Date of Submission

Fall 2022


Dr. Tariq Mehmood, Professor, Department of Computer Science, School of Mathematics and Computer Science (SMCS)


Anomaly detection is an important task in various fields, such as cybersecurity, financial fraud detection, and manufacturing quality control. The Gaussian mixture model (GMM) is an effective technique used for anomaly detection in multivariate data spaces due to its ability to estimate complex distributions. In this work, we propose an anomaly detection framework using GMM for detecting abnormal data points in highly imbalanced data. Our method entails training a GMM on the inlier's data and then using the trained model to find the Gaussian distribution with the maximum likelihood for outliers. Furthermore, we validate the estimated Gaussian distribution by measuring the Average statistical distance (Mahalanobis distance) between outliers and all trained Gaussians. We used different datasets from the UCI machine learning repository to validate our proposed methodology for anomaly detection. At last, we built a user-friendly interactive web app using Streamlit integrated with five solved anomaly detection problems of various domains to get a real-time experience of the framework.

Document Type

Restricted Access

Submission Type

Research Project

Available for download on Saturday, October 31, 2026

The full text of this document is only accessible to authorized users.