Baris Yamansavascilar, PhD

In this study, we introduce two machine and deep learning based approaches to network traffic classification using sub-flows and sliding windows for near-real-time use-cases such as providing QoS/QoE to application flows. We develop two approaches: (1) a packet-based sub-flow method that uses a fixed number of packets for each instance, and (2) a time-based subflow method that utilizes packets in a fixed time range for each instance. Unlike similar studies conducted in the literature, our methods are designed to be applicable to real-time use cases where the classification should give an accurate answer quickly after the start of a network traffic to provide the necessary QoS/QoE to the network flows. We have conducted experiments with both methods considering the efficient window sizes and sliding intervals. We present the results of two classification goals: (1) application identification and (2) application category identification. Our results show that the proposed time-based approach provides at least 89% accuracy in application identification (depending on the application), and 96% in application category identification which makes it highly accurate for use-cases to provide QoS/QoE to application flows.