Publication Date

Spring 2023

Degree Type

Master's Project

Degree Name

Master of Science in Data Science (MSDS)

Department

Computer Science

First Advisor

Mark Stamp

Second Advisor

William Andreopoulos

Third Advisor

Samanvitha Basole

Keywords

Machine Learning, Deep Learning, Classification, Darknet

Abstract

The darknet is frequently exploited for illegal purposes and activities, which makes darknet traffic detection an important security topic. Previous research has focused on various classification techniques for darknet traffic using machine learning and deep learning. We extend previous work by considering the effectiveness of a wide range of machine learning and deep learning technique for the classification of darknet traffic by application type. We consider the CICDarknet2020 dataset, which has been used in many previous studies, thus enabling a direct comparison of our results to previous work. We find that XGBoost performs the best among the classifiers that we have tested.

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