Encrypted IoT Network Traffic Analysis for Cybersecurity

Use of clear-text for network communication is quickly becoming obsolete. End-to-end encryption of mobile
applications and proliferation of TLS based encrypted traffic are fueling the growth of encrypted traffic on the
Internet. Many applications tunnel traffic over HTTPS. Today, over 80% of Internet traffic is encrypted. With the
recent advancement of TLS handshake, traditional mechanisms such as Deep Packet Inspection (DPI) and TLS
fingerprinting are unable to inspect traffic in the traditional manner. As a result, operators are unable to block
illegal applications, enforce policies, and detect cyber threats hiding in an encrypted traffic. In addition, extensive
deployment of hyperconnected devices such as Internet of Things (IoT) has created a new attack surface. Further,
an emerging generation of IoT devices will support encryption, making detection of rogue IoT devices a challenge.
In this project our goal is to design, develop and evaluate a machine learning based platform for encrypted network
traffic analysis in order to detect rogue IoT devices and malicious behaviours.

Faculty Supervisor:

Nur Zincir-Heywood

Student:

Partner:

Solana Networks

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Dalhousie University

Program: