Encrypted Traffic Classification (Internship)

Overview

About the project

A notable trend in the rapidly evolving mobile technology domain is the increasing reliance on encrypted network packets to strengthen privacy and security. Nevertheless, certain unencrypted elements, such as packet size and other critical Internet functionalities, remain exposed despite this encryption. Utilising Deep Learning models, the tool is designed to categorise the traffic on a network and deduce the types of applications on mobile phones. Positioned at the intersection of network security and AI-driven analysis, this innovative project emphasises developing and refining cutting-edge deep-learning models. These models are specifically designed to pinpoint the applications generating network traffic despite encryption.

This   Encrypted Traffic Classification (Internship) project from King Abdullah University Of Science and Technology (KAUST) is committed to privacy, and ethical considerations are a vital aspect of this project. While encrypted packets bolster security, the ability of tools to categorise their contents and deduce installed applications poses privacy challenges.