Classification of Encrypted Traffic using Neural Networks
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Description
This is an Open Access article published on 14 January 2020, which successfully uses neural networks to identify encrypted internet traffic and predict, with significant accuracy, the type of content being transmitted (i.e. video streaming, voice calls, emails, etc). The paper makes no mention of ways to mitigate the privacy implications of such an implementation of neural networks. However, it provides a sobering picture of the future of encryption services such a VPN, TOR and SSL/TSL, which are liable to become obsolete in the face of such implementations of neural networks.
Abstract
"Encrypted traffic classification plays a vital role in cybersecurity as network traffic encryption becomes prevalent. First, we briefly introduce three traffic encryption mechanisms: IPsec, SSL/TLS, and SRTP. After evaluating the performances of support vector machine, random forest, naïve Bayes, and logistic regression for traffic classification, we propose the combined approach of entropy estimation and artificial neural networks. First, network traffic is classified as encrypted or plaintext with entropy estimation. Encrypted traffic is then further classified using neural networks. We propose using traffic packet’s sizes, packet's inter‐arrival time, and direction as the neural network's input. Our combined approach was evaluated with the dataset obtained from the Canadian Institute for Cybersecurity. Results show an improved precision (from 1 to 7 percentage points), and some application classification metrics improved nearly by 30 percentage points."
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TextAcademic Paper / Journal