1.
Wehner, N., Ring, M., Schüler, J., Hotho, A., Hoßfeld, T., Seufert, M.: On Learning Hierarchical Embeddings from Encrypted Network Traffic. 33th IEEE/IFIP Network Operations and Management Symposium (NOMS). , Budapest, Hungary (2022).
This work presents a novel concept for learning embeddings from encrypted network traffic. In contrast to existing approaches, we evaluate the feasibility of hierarchical embeddings by iteratively aggregating packet embeddings to flow embeddings, and flow embeddings to trace embeddings. The hierarchical embedding concept was designed to especially consider complex dependencies of Internet traffic on different time scales. We describe this novel embedding concept for the domain of network traffic in full detail, and evaluate its performance for the downstream task of website fingerprinting, i.e., identifying websites from encrypted traffic, which is relevant for network management, e.g., as a prerequisite for QoE monitoring or for intrusion detection. Our evaluation reveals that embeddings are a promising solution for website fingerprinting as our model correctly labels up to 99.8% of traces from 500 target websites.
2.
Casas, P., Wassermann, S., Wehner, N., Seufert, M., Schüler, J., Hoßfeld, T.: Mobile Web and App QoE Monitoring for ISPs - from Encrypted Traffic to Speed Index through Machine Learning. 13th Wireless and Mobile Networking Conference (WMNC), Best Paper Award. , Montreal, Canada (Virtual Conference) (2021).
3.
Wehner, N., Seufert, M., Schüler, J., Casas, P., Hoßfeld, T.: How are your Apps Doing? QoE Inference and Analysis in Mobile Devices. 17th International Conference on Network and Service Management (CNSM). , Izmir, Turkey (Virtual Conference) (2021).
Web browsing has become the most important application of the Internet for the end user. When it comes to mobile devices, web services are mainly accessed through apps. This paper tackles the problem of Web Quality of Experience (QoE) in mobile devices, with a specific focus on apps QoE monitoring and analysis, using in-network (encrypted) traffic measurements. Measuring apps QoE is complex, not only from an instrumentation point of view, but also from the heterogeneity of user interactions which might realize substantially different user experience. To this end, we conduct a feasibility study on four specific and popular Android apps and their corresponding web services. Our test automation framework emulates and measures different user interactions commonly executed during an app session, including the app startup, clicking, scrolling, and searching. The resulting traffic is characterized on different dimensions, and machine learning models are trained to identify web services, apps, and user interactions, and to infer their QoE. The proposed models can correctly identify the specific web service and app in 86% of the cases and accurately estimate the associated QoE with small errors. Our preliminary study represents a first step towards an in-network, web QoE monitoring solution for mobile-device apps.
4.
Wehner, N., Seufert, M., Wieser, V., Casas, P., Capdehourat, G.: Quality that Matters: QoE Monitoring in Education Service Provider (ESP) Networks. IFIP/IEEE International Symposium on Integrated Network Management (IM 2021). , Bordeaux, France (Virtual Conference) (2021).
Education Service Providers (ESPs) play a crucial role in the digitization of education as they equip students and teachers with reliable devices and provide high quality Internet access at schools. This paper investigates four months worth of continuous measurements conducted by an ESP using a largescale, passive, in-device Quality of Experience (QoE) monitoring system deployed into a nationwide network of education-purpose devices. These measurements cover more than 800 education centers and about 4000 devices, used both in schools and at home. Using this rich dataset, we present an exhaustive characterization of the browsing behavior, and a quantification of the web and video QoE in this educational context. Web QoE results showed a better performance for school Wi-Fi networks compared to home connections, suggesting that several issues may arise for ESPs due to the increasing relevance of home-schooling caused by the COVID-19 pandemic.
5.
Grigorjew, A., Seufert, M., Wehner, N., Hofmann, J., Hoßfeld, T.: ML-Assisted Latency Assignments in Time-Sensitive Networking. IFIP/IEEE International Symposium on Integrated Network Management (IM 2021). , Bordeaux, France (Virtual Conference) (2021).
Recent developments in industrial automation and invehicle communication have raised the requirements of real-time networking. Bus systems that were traditionally deployed in these fields cannot provide sufficient bandwidth and are now shifting towards Ethernet for their real-time communication needs. In this field, standardization efforts from the IEEE and the IETF have developed new data plane mechanisms such as shapers and schedulers, as well as control plane mechanisms such as reservation protocols to support their new requirements. However, their implementation and their optimal configuration remain an important factor for their efficiency. This work presents a machine learning framework that takes on the configuration task. Four different models are trained for the configuration of per-hop latency guarantees in a distributed resource reservation process and compared with respect to their real-time traffic capacity. The evaluation shows that all models provide good configurations for the provided scenarios, but more importantly, they represent a first step for a semi-automated configuration of parameters in Time-Sensitive Networking.
