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A Deep Packet Inspection application that provides encrypted protocol recognition based on Machine Learning
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Traffic-Detector

A Deep Packet Inspection application that provides encrypted protocol recognition based on Machine Learning.

This project implements packet-based encrypted traffic classification. In particular we are interested in analyzing traffic from six different cases of some of the most mainstream protocols and applications. More specifically we aim to examine Tor, SSL (web), BitTorrent PE, SSH (shell session), SSH (SCP) and Skype. In addition in order to make the project more challenging we assume that we want to distinguish the protocols without having any idea about the initial handshake, which is something that would reveal useful information about the protocol. This assumption describes a real situation as a possible passive adversary would be able to eavesdrop a communication anytime, without having any information about the initial handshakes that are necessary for establishing the connection. Moreover as we do not study the general behavior, we assume that the study is port independent, and as a result those features are not under consideration. In order to increase our chances of achieving good classification accuracy, we recruited state-of-the-art classifiers of Machine Learning. In particular we tried five classifiers such as Support Vector Machines, MLP Neural Networks, Bayesian Networks, C4.5 and Logistic Regression in order to evaluate their accuracy on identifying the correct communication protocol in multiclass and binary classifications. C4.5 algorithm proved the most accurate classifier for our multiclass dataset. For this reason we implemented a system for real time encrypted traffic classification based on the C4.5 Decision Tree and a fixed upper bound of time for traffic sampling.


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