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Project Title: Identifying Communities in Network Traffic: SSH Classification
Participants: Riyad Alshammari, Dr. Malcolm Heywood, Dr. Nur Zincir-Heywood, Dr. Jeannette Janssen, Dr. Evangelos Milios,
Project Description: Accurate identification of network traffic according to the application type is an important task of network management. For example, a network administrator may want to identify and throttle/block traffic from peer to peer applications to manage bandwidth budget and to ensure quality of service objectives are met for business critical applications. Similar to network management tasks, many network engineering problems such as workload characterization, and modeling, capacity planning, traffic shaping/ policing and route provisioning also rely on accurate identification of network traffic. However, an accurate method for reliably identifying the applications associated with network traffic is still to be developed. In this work, we are specifically interested in automatically detecting/identifying ssh traffic using a hybrid approach including graph and machine learning based techniques.