Our latest research paper in ACM TOMM focuses on how video streaming systems can better utilize available bandwidth to provide users with an improved Quality of Experience (QoE).

Our paper “(Not) The Sum of Its Parts: Relating Individual Video and Browsing Stimuli to Web Session QoE” got presented at the 16th International Conference on Quality of Multimedia Experience (QoMEX). This paper investigates the Quality of Experience (QoE) in web sessions that combine both web browsing and video streaming stimuli, addressing the gap in understanding session-level QoE and proposing models to estimate it based on individual stimuli.

Unser Paper ?QoEXplainer: Mediating Explainable Quality of Experience Models with Large Language Models“ wurde auf der 16th International Conference on Quality of Multimedia Experience (QoMEX) vorgestellt. Das Papier stellt QoEXplainer vor, ein Dashboard, das gro?e Sprachmodelle und die Verwendung von Mediatoren verwendet, um erkl?rbare, datengesteuerte Quality of Experience (QoE) Modelle zu veranschaulichen und den Benutzern zu helfen, die Beziehungen zwischen den Modellen durch eine interaktive Chatbot-Schnittstelle zu verstehen.

Our paper “Sitting, Chatting, Waiting: Influence of Loading Times on Mobile Instant Messaging QoE” got presented at the 16th International Conference on Quality of Multimedia Experience (QoMEX). The paper examines the relationship between loading times and user experience (QoE) in mobile instant messaging applications and shows that longer loading times reduce user acceptance and satisfaction, although they do not directly influence QoE ratings.

Prof. Dr. Michael Seufert, Inhaber des Lehrstuhls für Vernetzte Eingebettete Systeme und Kommunikationssysteme, hat den diesj?hrigen CNOM Young Professional Award des Institute of Electrical and Electronics Engineers Communications Society Technical Committee on Network Operation and Management gewonnen.

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Our paper “HALIDS: a Hardware-Assisted Machine Learning IDS for in-Network Monitoring” was published in the 8th Network Traffic Measurement and Analysis (TMA) Conference.?The paper presents HALIDS, a prototype of a Machine Learning-driven Intrusion Detection System that enables network devices to autonomously make security decisions using in-band and off-band traffic analysis, ultimately aiming to enhance network security through faster processing and intelligent decision-making.

Our paper “The Missing Link in Network Intrusion Detection: Taking AI/ML Research Efforts to Users” was published in IEEE Access. The paper focuses on the challenges faced in adopting Artificial Intelligence (AI) and Machine Learning (ML) within Intrusion Detection Systems (IDS). It identifies barriers to implementation, such as the lack of explainability, usability, and privacy considerations that hinder trust among non-expert users. The authors employ a user-centric approach by examining IDS research through the lens of various stakeholders, deriving realistic personas, and proposing design guidelines and hypotheses to enhance practical adoption of AI/ML-based IDS solutions.

Prof. Dr. Michael Seufert was invited by Deutschlandfunk to talk about our new system for real-time evaluation of the quality of Internet data streams. The interview appeared in the program “Forschung aktuell - Computer und Kommunikation” and was broadcast on May 11, 2024.

Our latest paper is a good starting point for newcomers to the fields of artificial intelligence and communication networks. It provides a comprehensive overview for researchers investigating the application of machine learning to optimize communication networks and the use of these networks to improve training processes in machine learning.

Katharina Dietz, PhD candidate at the Chair of Communication Networks at the University of Würzburg, visited our chair and gave a talk on "User-based active learning for network monitoring tasks". The presentation was based on a joint publication and current joint research activities and was subsequently discussed with staff and interested students.

Moderne KI-Verfahren sollen künftig den Verkehr im Internet so verteilen, dass sich m?glichst niemand ausgebremst fühlt. An der Universit?t Augsburg wurde jetzt ein System vorgestellt, das die Qualit?t sehr vieler Datenstr?me in Echtzeit bewerten kann. Das gilt als eine Grundvoraussetzung, um durch ein besseres Daten-Management die Zufriedenheit im Netz zu verbessern.

Our paper “Marina: Realizing ML-Driven Real-Time Network Traffic Monitoring at Terabit Scale” was published in the IEEE Transactions on Network and Service Management (TNSM) Journal. The paper describes “Marina”, a system designed to improve real-time monitoring of network traffic to ensure both performance and security for customers on large networks. It utilizes an efficient data plane to collect real-time traffic statistics and a powerful ML server to run complex ML models, enabling it to monitor more than 520,000 concurrent connections at a total capacity of 6.4 Tbps and achieve comparable or better results than existing solutions.