Anomaly Detection in Network Traffic

This repository contains resources and documentation for our project on anomaly detection in network traffic, conducted as part of the Data Communication Networks course at Texas A&M University - Corpus Christi.

Abstract

We tackle the challenge of detecting anomalies in network traffic to enhance information security. By analyzing a dataset with diverse network interaction attributes, we deploy multiple machine learning models, including Logistic Regression, Random Forest, SVM, and others. Our approach emphasizes handling data imbalances and transforming categorical data into numerical formats. We critically assess model performance through accuracy, precision, recall, and F1-score metrics.

Team Members

• Om Preetham Bandi
• Akhil Polsani
• Thriveen Ullendula
• Reddy Bhuvan Korlakunta

Repository Contents

• Code: Contains all scripts and notebooks for data preprocessing, model building, and evaluation.
• Presentation Slides: Slides used during the project presentation, detailing methodology, results, and insights.
• Project Paper: Comprehensive paper that discusses the project's approach, findings, and implications for network security.