Project Title : Darknet Traffic Classification using Machine Learning Techniques

Team Members

Dr. G. Padmavathi, Dean - PSCS, Professor, Department of Computer Science

Ms. A. Roshni, Research Assistant, Centre for Cyber Intelligence, DST - CURIE - AI

Ms. Sahithi Kasim, B.Tech Computer Science and Engineering, G. Narayanamma Institute of Technology and Science, Hyderabad, Telangana

Project Summary

Darknet traffic classification is playing an important to categorize real-time applications it is an unused address space used in the internet. Analyzing darknet traffic aids in early detection of malware and early monitoring of malware before it outbreaks.To identify Darknet traffic, we used machine learning methods. To provide a better visual representation of the results, a ROC curve is used and a feature selection analysis is used for the better classifier results. The experiments were carried out on the CIC-Darknet2020 dataset. Traffic is divided into two categories: "Benign" and "Darknet,"where"Tor" and "VPN"are considered into"Darknet" category and "Non Tor" and "Non VPN"are considered into"Benign" category. Using several supervised machine learning approaches, like Logistic Regression, Support Vector Machine, Naive Bayes, K-Nearest Neighbors and Decision Tree Classifier an average prediction accuracy of over 99% was achieved.