LAUSR

It has been well established that the Internet of Things will bring an expansion in traffic volume and types. This will bring new challenges in terms of Quality of Service (QoS) and security, requiring innovative traffic management techniques. Traffic classification is a main network function that helps in managing both QoS and security. Different machine learning based methods have been applied for this aim. However, traditional machine learning methods rely on hand crafted features, limiting the model ability to learn. Deep Learning (DL), a branch of machine learning, is characterized by its representation learning ability. In this paper, we analyse two methods of data representation for DL-based classification: a raw packet-based representation and a quasi-raw flow-based representation. Different tests are performed to evaluate the robustness of these data representation methods. The tests include features’ importance, model robustness, and anonymization tests. The results show that raw data representation suffers from traffic anonymization and the fact that many packet fields are data-dependent. On the other hand, the flow-based representation is sensitive to the number of packets used for classification and to traffic obfuscation.