Abstract Machine learning (ML)-based traffic classification is evolving into a well-established
research domain. Considering statistical characteristics of the traffic flows, ML-based …

With the increase in the proportion of encrypted network traffic, encrypted traffic identification
(ETI) is becoming a critical research topic for network management and security. At present …

Traffic classification, ie the inference of applications and/or services from their network traffic,
represents the workhorse for service management and the enabler for valuable profiling …

Recent attempts to introduce the Generative Adversarial Network (GAN) to the computer
network traffic domain have shown promise, including several frameworks which generate …

In the context of the proliferated evolution of network service types and the expeditious
augmentation of network resource deployment, the requisition for copious labeled datasets …

The need to develop more efficient network traffic data generation techniques that can
reproduce the intricate features of traffic flows forms a central element in secured monitoring …

Traffic classification, ie associating network traffic to the application that generated it, is an
important tool for several tasks, spanning on different fields (security, management, traffic …

Network traffic classification is used in many applications including network provisioning,
malware detection, resource management, and so on. In modern networks, use of encrypted …

Deep learning models have achieved great success in recent years but progress in some
domains like cybersecurity is stymied due to a paucity of realistic datasets. Organizations are …

Network traffic classification plays a significant role in cyber security applications and
management scenarios. Conventional statistical classification techniques rely on the …