Nikolas Wehner - Lehrstuhl für Informatik III

Telefon	(0931) 31-88150
Telefax	(0931) 31-86632
E-Mail	nikolas.wehner@uni-wuerzburg.de
Raum	A213a
Anschrift	Lehrstuhl für Informatik III Am Hubland D-97074 Würzburg

Contents: Overview | Publications | Teaching | Working Group | Projects

Research Interests

My research interests include the following topics:

Quality of Experience (QoE) of Internet applications
Measurement and analytics of (encrypted) network traffic
Artificial intelligence and machine learning for networks
Performance analysis and modeling of communication systems

Recent Publications

Do you Agree? Contrasting Google’s Core Web Vitals and the Impact of Cookie Consent Banners with Actual Web QoE Wehner, Nikolas; Seufert, Michael; Schatz, Raimund; Hoßfeld, Tobias in Quality and User Experience (2023).
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
Providing sophisticated web Quality of Experience (QoE) has become paramount for web service providers and network operators alike. Due to advances in web technologies (HTML5, respon- sive design, etc.), traditional web QoE models focusing mainly on loading times have to be refined and improved. In this work, we relate Google’s Core Web Vitals, a set of metrics for improving user experience, to the loading time aspects of web QoE, and investigate whether the Core Web Vitals and web QoE agree on the perceived experience. To this end, we first perform objective measurements in the web using Google’s Lighthouse. To close the gap be- tween metrics and experience, we complement these objective measurements with subjective assessment by performing multiple crowdsourcing QoE studies. For this purpose, we developed CWeQS, a customized framework to emulate the entire web page loading process, and ask users for their experience while controlling the Core Web Vitals, which is available to the public. To properly configure CWeQS for the planned QoE study and the crowdsourcing setup, we conduct pre-studies, in which we evaluate the importance of the loading strategy of a web page and the impor- tance of the user task. The obtained insights allow us to conduct the desired QoE studies for each of the Core Web Vitals. Furthermore, we assess the impact of cookie consent banners, which have become ubiquitous due to regulatory demands, on the Core Web Vitals and investigate their influence on web QoE. Our results suggest that the Core Web Vitals are much less predictive for web QoE than expected and that page loading times remain the main metric and influence factor in this context. We further observe that unobtrusive and acentric cookie consent banners are preferred by end-users and that addi- tional delays caused by interacting with consent banners in order to agree to or reject cookies should be accounted along with the actual page load time to reduce waiting times and thus to improve web QoE.

@article{info3-article-2023-3, abstract = {Providing sophisticated web Quality of Experience (QoE) has become paramount for web service providers and network operators alike. Due to advances in web technologies (HTML5, respon- sive design, etc.), traditional web QoE models focusing mainly on loading times have to be refined and improved. In this work, we relate Google’s Core Web Vitals, a set of metrics for improving user experience, to the loading time aspects of web QoE, and investigate whether the Core Web Vitals and web QoE agree on the perceived experience. To this end, we first perform objective measurements in the web using Google’s Lighthouse. To close the gap be- tween metrics and experience, we complement these objective measurements with subjective assessment by performing multiple crowdsourcing QoE studies. For this purpose, we developed CWeQS, a customized framework to emulate the entire web page loading process, and ask users for their experience while controlling the Core Web Vitals, which is available to the public. To properly configure CWeQS for the planned QoE study and the crowdsourcing setup, we conduct pre-studies, in which we evaluate the importance of the loading strategy of a web page and the impor- tance of the user task. The obtained insights allow us to conduct the desired QoE studies for each of the Core Web Vitals. Furthermore, we assess the impact of cookie consent banners, which have become ubiquitous due to regulatory demands, on the Core Web Vitals and investigate their influence on web QoE. Our results suggest that the Core Web Vitals are much less predictive for web QoE than expected and that page loading times remain the main metric and influence factor in this context. We further observe that unobtrusive and acentric cookie consent banners are preferred by end-users and that addi- tional delays caused by interacting with consent banners in order to agree to or reject cookies should be accounted along with the actual page load time to reduce waiting times and thus to improve web QoE.}, author = {Wehner, Nikolas and Seufert, Michael and Schatz, Raimund and Hoßfeld, Tobias}, journal = {Quality and User Experience}, keywords = {ucn}, title = {Do you Agree? Contrasting Google’s Core Web Vitals and the Impact of Cookie Consent Banners with Actual Web QoE}, year = 2023 }

%0 Journal Article %1 info3-article-2023-3 %A Wehner, Nikolas %A Seufert, Michael %A Schatz, Raimund %A Hoßfeld, Tobias %D 2023 %J Quality and User Experience %T Do you Agree? Contrasting Google’s Core Web Vitals and the Impact of Cookie Consent Banners with Actual Web QoE %X Providing sophisticated web Quality of Experience (QoE) has become paramount for web service providers and network operators alike. Due to advances in web technologies (HTML5, respon- sive design, etc.), traditional web QoE models focusing mainly on loading times have to be refined and improved. In this work, we relate Google’s Core Web Vitals, a set of metrics for improving user experience, to the loading time aspects of web QoE, and investigate whether the Core Web Vitals and web QoE agree on the perceived experience. To this end, we first perform objective measurements in the web using Google’s Lighthouse. To close the gap be- tween metrics and experience, we complement these objective measurements with subjective assessment by performing multiple crowdsourcing QoE studies. For this purpose, we developed CWeQS, a customized framework to emulate the entire web page loading process, and ask users for their experience while controlling the Core Web Vitals, which is available to the public. To properly configure CWeQS for the planned QoE study and the crowdsourcing setup, we conduct pre-studies, in which we evaluate the importance of the loading strategy of a web page and the impor- tance of the user task. The obtained insights allow us to conduct the desired QoE studies for each of the Core Web Vitals. Furthermore, we assess the impact of cookie consent banners, which have become ubiquitous due to regulatory demands, on the Core Web Vitals and investigate their influence on web QoE. Our results suggest that the Core Web Vitals are much less predictive for web QoE than expected and that page loading times remain the main metric and influence factor in this context. We further observe that unobtrusive and acentric cookie consent banners are preferred by end-users and that addi- tional delays caused by interacting with consent banners in order to agree to or reject cookies should be accounted along with the actual page load time to reduce waiting times and thus to improve web QoE.
Explainable Data-Driven QoE Modelling using XAI Wehner, Nikolas; Seufert, Anika; Hoßfeld, Tobias; Seufert, Michael in 15th International Conference on Quality of Multimedia Experience (QoMEX) (2023).
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
Data-driven QoE modelling using Machine Learning (ML) allows to reduce the modelling bias and to continuously integrate new QoE results into the QoE model, which can improve its generalizability. The downside is that the majority of ML models are black-box models, which prevent to obtain insights about QoE influence factors and their fundamental relationships that are highly relevant for researchers and providers of services and networks. However, recent advances in the field of eXplainable Artificial Intelligence (XAI) resolve these issues. Thus, XAI allows to benefit from data-driven QoE modelling to obtain generalizable QoE models, and at the same time to understand what QoE factors are relevant and how they affect the QoE score. In this work, we showcase the feasibility of explainable data-driven QoE modelling for video streaming, since video streaming QoE has been well researched, and thus, allows us to validate our results. Finally, we discuss opportunities and challenges of deploying XAI for QoE modelling.

@inproceedings{info3-inproceedings-2023-4, abstract = {Data-driven QoE modelling using Machine Learning (ML) allows to reduce the modelling bias and to continuously integrate new QoE results into the QoE model, which can improve its generalizability. The downside is that the majority of ML models are black-box models, which prevent to obtain insights about QoE influence factors and their fundamental relationships that are highly relevant for researchers and providers of services and networks. However, recent advances in the field of eXplainable Artificial Intelligence (XAI) resolve these issues. Thus, XAI allows to benefit from data-driven QoE modelling to obtain generalizable QoE models, and at the same time to understand what QoE factors are relevant and how they affect the QoE score. In this work, we showcase the feasibility of explainable data-driven QoE modelling for video streaming, since video streaming QoE has been well researched, and thus, allows us to validate our results. Finally, we discuss opportunities and challenges of deploying XAI for QoE modelling.}, address = {Ghent, Belgium}, author = {Wehner, Nikolas and Seufert, Anika and Hoßfeld, Tobias and Seufert, Michael}, booktitle = {15th International Conference on Quality of Multimedia Experience (QoMEX)}, keywords = {ucn}, month = 6, title = {Explainable Data-Driven QoE Modelling using XAI}, year = 2023 }

%0 Conference Paper %1 info3-inproceedings-2023-4 %A Wehner, Nikolas %A Seufert, Anika %A Hoßfeld, Tobias %A Seufert, Michael %B 15th International Conference on Quality of Multimedia Experience (QoMEX) %C Ghent, Belgium %D 2023 %T Explainable Data-Driven QoE Modelling using XAI %X Data-driven QoE modelling using Machine Learning (ML) allows to reduce the modelling bias and to continuously integrate new QoE results into the QoE model, which can improve its generalizability. The downside is that the majority of ML models are black-box models, which prevent to obtain insights about QoE influence factors and their fundamental relationships that are highly relevant for researchers and providers of services and networks. However, recent advances in the field of eXplainable Artificial Intelligence (XAI) resolve these issues. Thus, XAI allows to benefit from data-driven QoE modelling to obtain generalizable QoE models, and at the same time to understand what QoE factors are relevant and how they affect the QoE score. In this work, we showcase the feasibility of explainable data-driven QoE modelling for video streaming, since video streaming QoE has been well researched, and thus, allows us to validate our results. Finally, we discuss opportunities and challenges of deploying XAI for QoE modelling.
Fingerprinting Webpages and Smartphone Apps from Encrypted Network Traffic with WebScanner Casas, Pedro; Wehner, Nikolas; Wassermann, Sarah; Seufert, Michael in Global Internet (GI) Symposium (2022).
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
The wide adoption of end-to-end encryption drastically limits the visibility Internet Service Providers (ISPs) have on the services consumed by their customers. This is particularly challenging for monitoring and analysis of web and apps traffic, which is highly complex and heterogeneous. Loading a web page or app today requires tens of flows to download the various resources located in distributed cloud servers from different content providers. We introduce WebScanner, a webpage and app fingerprinting approach capable to identify all the traffic flows corresponding to individual webpage and app loading sessions within concurrent web pages traffic, enabling highly detailed, per webpage analysis in practical deployments. Different from the state of the art in web and app traffic fingerprinting, and as our most relevant contribution, WebScanner automatically performs the parsing of all the traffic generated by a web visit and its isolation from concurrent traffic, instead of assuming that an external oracle system does so. WebScanner also implements a deep fingerprinting approach to detect user action-dependent traffic from apps, relying on simple machine learning models and strong input features as fingerprints. Extensive evaluation across a large measurement dataset of popular webpages and mobile apps confirms the outstanding performance of WebScanner, identifying the top-500 Alexa websites with precision and recall (P/R) above 95%, isolating their full contents with P/R above 80% for up to 15 concurrent webpages, and detecting specific action-dependent apps traffic with average P/R above 92%.

@inproceedings{info3-inproceedings-2022-18, abstract = {The wide adoption of end-to-end encryption drastically limits the visibility Internet Service Providers (ISPs) have on the services consumed by their customers. This is particularly challenging for monitoring and analysis of web and apps traffic, which is highly complex and heterogeneous. Loading a web page or app today requires tens of flows to download the various resources located in distributed cloud servers from different content providers. We introduce WebScanner, a webpage and app fingerprinting approach capable to identify all the traffic flows corresponding to individual webpage and app loading sessions within concurrent web pages traffic, enabling highly detailed, per webpage analysis in practical deployments. Different from the state of the art in web and app traffic fingerprinting, and as our most relevant contribution, WebScanner automatically performs the parsing of all the traffic generated by a web visit and its isolation from concurrent traffic, instead of assuming that an external oracle system does so. WebScanner also implements a deep fingerprinting approach to detect user action-dependent traffic from apps, relying on simple machine learning models and strong input features as fingerprints. Extensive evaluation across a large measurement dataset of popular webpages and mobile apps confirms the outstanding performance of WebScanner, identifying the top-500 Alexa websites with precision and recall (P/R) above 95%, isolating their full contents with P/R above 80% for up to 15 concurrent webpages, and detecting specific action-dependent apps traffic with average P/R above 92%.}, address = {Paris, France}, author = {Casas, Pedro and Wehner, Nikolas and Wassermann, Sarah and Seufert, Michael}, booktitle = {Global Internet (GI) Symposium}, keywords = {ucn}, month = 11, title = {Fingerprinting Webpages and Smartphone Apps from Encrypted Network Traffic with WebScanner}, year = 2022 }

%0 Conference Paper %1 info3-inproceedings-2022-18 %A Casas, Pedro %A Wehner, Nikolas %A Wassermann, Sarah %A Seufert, Michael %B Global Internet (GI) Symposium %C Paris, France %D 2022 %T Fingerprinting Webpages and Smartphone Apps from Encrypted Network Traffic with WebScanner %X The wide adoption of end-to-end encryption drastically limits the visibility Internet Service Providers (ISPs) have on the services consumed by their customers. This is particularly challenging for monitoring and analysis of web and apps traffic, which is highly complex and heterogeneous. Loading a web page or app today requires tens of flows to download the various resources located in distributed cloud servers from different content providers. We introduce WebScanner, a webpage and app fingerprinting approach capable to identify all the traffic flows corresponding to individual webpage and app loading sessions within concurrent web pages traffic, enabling highly detailed, per webpage analysis in practical deployments. Different from the state of the art in web and app traffic fingerprinting, and as our most relevant contribution, WebScanner automatically performs the parsing of all the traffic generated by a web visit and its isolation from concurrent traffic, instead of assuming that an external oracle system does so. WebScanner also implements a deep fingerprinting approach to detect user action-dependent traffic from apps, relying on simple machine learning models and strong input features as fingerprints. Extensive evaluation across a large measurement dataset of popular webpages and mobile apps confirms the outstanding performance of WebScanner, identifying the top-500 Alexa websites with precision and recall (P/R) above 95%, isolating their full contents with P/R above 80% for up to 15 concurrent webpages, and detecting specific action-dependent apps traffic with average P/R above 92%.
A Vital Improvement? Relating Google’s Core Web Vitals to Actual Web QoE Wehner, Nikolas; Amir, Monisha; Seufert, Michael; Schatz, Raimund; Hoßfeld, Tobias in 14th International Conference on Quality of Multimedia Experience (QoMEX), Best Paper Award (2022).
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
Providing sophisticated Web Quality of Experience (QoE) has become paramount for web service providers and network operators alike. Due to advances in Web technologies (HTML5, responsive design, etc.), traditional Web QoE models focusing mainly on loading times have to be refined and improved. In this work, we relate Google's Core Web Vitals, a set of metrics for improving user experience, to the loading time aspects of Web QoE. To this end, we first perform objective measurements in the Web using Google's Lighthouse. To close the gap between metrics and experience, we complement these objective measurements with subjective assessment by performing multiple crowdsourcing QoE studies. In these studies, we use CWeQS, a customized framework to emulate the entire web page loading process, and ask users for their experience while controlling the Core Web Vitals. Our results suggest that the Core Web Vitals have less predictive value for Web QoE than expected and that page loading times remain the main influence factor in this context.

@inproceedings{info3-inproceedings-2022-12, abstract = {Providing sophisticated Web Quality of Experience (QoE) has become paramount for web service providers and network operators alike. Due to advances in Web technologies (HTML5, responsive design, etc.), traditional Web QoE models focusing mainly on loading times have to be refined and improved. In this work, we relate Google's Core Web Vitals, a set of metrics for improving user experience, to the loading time aspects of Web QoE. To this end, we first perform objective measurements in the Web using Google's Lighthouse. To close the gap between metrics and experience, we complement these objective measurements with subjective assessment by performing multiple crowdsourcing QoE studies. In these studies, we use CWeQS, a customized framework to emulate the entire web page loading process, and ask users for their experience while controlling the Core Web Vitals. Our results suggest that the Core Web Vitals have less predictive value for Web QoE than expected and that page loading times remain the main influence factor in this context.}, address = {Lippstadt, Germany}, author = {Wehner, Nikolas and Amir, Monisha and Seufert, Michael and Schatz, Raimund and Hoßfeld, Tobias}, booktitle = {14th International Conference on Quality of Multimedia Experience (QoMEX), Best Paper Award}, keywords = {ucn}, month = 9, title = {A Vital Improvement? Relating Google's Core Web Vitals to Actual Web QoE}, year = 2022 }

%0 Conference Paper %1 info3-inproceedings-2022-12 %A Wehner, Nikolas %A Amir, Monisha %A Seufert, Michael %A Schatz, Raimund %A Hoßfeld, Tobias %B 14th International Conference on Quality of Multimedia Experience (QoMEX), Best Paper Award %C Lippstadt, Germany %D 2022 %T A Vital Improvement? Relating Google's Core Web Vitals to Actual Web QoE %X Providing sophisticated Web Quality of Experience (QoE) has become paramount for web service providers and network operators alike. Due to advances in Web technologies (HTML5, responsive design, etc.), traditional Web QoE models focusing mainly on loading times have to be refined and improved. In this work, we relate Google's Core Web Vitals, a set of metrics for improving user experience, to the loading time aspects of Web QoE. To this end, we first perform objective measurements in the Web using Google's Lighthouse. To close the gap between metrics and experience, we complement these objective measurements with subjective assessment by performing multiple crowdsourcing QoE studies. In these studies, we use CWeQS, a customized framework to emulate the entire web page loading process, and ask users for their experience while controlling the Core Web Vitals. Our results suggest that the Core Web Vitals have less predictive value for Web QoE than expected and that page loading times remain the main influence factor in this context.
X-Ray Goggles for the ISP: Improving in-Network Web and App QoE Monitoring with Deep Learning Casas, Pedro; Wassermann, Sarah; Seufert, Michael; Wehner, Nikolas; Dinica, Olivia; Hoßfeld, Tobias in Network Traffic Measurement and Analysis Conference (TMA) (2022).
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
The wide adoption of end-to-end encryption is drastically limiting the visibility Internet Service Providers (ISPs) have on the performance of the services consumed by their customers. In times of strong competition, where customer experience plays a key role in churn management, ISPs require novel solutions enabling network-wide Quality of Experience (QoE) monitoring. To this end, we present DeepQoE, a deep-learning based approach to infer the QoE of web services and mobile applications from the ISP perspective, relying exclusively on the analysis of encrypted network traffic. Using raw features derived from the encrypted stream of bytes as input to deep Convolutional Neural Networks (CNNs), DeepQoE infers the Speed Index of web browsing sessions and general mobile apps with unprecedented accuracy, improving the state of the art by more than 25%, and reducing the QoE inference error in terms of mean opinion scores by nearly 40%. DeepQoE implements a web fingerprinting solution to identify individual web browsing sessions within concurrent web pages traffic, enabling highly detailed, per web page QoE inference in practical deployments. Extensive evaluations over a large and heterogeneous dataset composed of web and app measurements, using different device types and for top-popular websites and apps, confirm the out-performance of DeepQoE over previously used shallow-learning models, as well as the deep-model generalization to different devices, web pages, apps, and network setups. DeepQoE is the first deployable system providing such a deep, highly-detailed QoE for individual web browsing and mobile apps over encrypted traffic, using deep learning models on heterogeneous measurements.

@inproceedings{info3-inproceedings-2022-11, abstract = {The wide adoption of end-to-end encryption is drastically limiting the visibility Internet Service Providers (ISPs) have on the performance of the services consumed by their customers. In times of strong competition, where customer experience plays a key role in churn management, ISPs require novel solutions enabling network-wide Quality of Experience (QoE) monitoring. To this end, we present DeepQoE, a deep-learning based approach to infer the QoE of web services and mobile applications from the ISP perspective, relying exclusively on the analysis of encrypted network traffic. Using raw features derived from the encrypted stream of bytes as input to deep Convolutional Neural Networks (CNNs), DeepQoE infers the Speed Index of web browsing sessions and general mobile apps with unprecedented accuracy, improving the state of the art by more than 25%, and reducing the QoE inference error in terms of mean opinion scores by nearly 40%. DeepQoE implements a web fingerprinting solution to identify individual web browsing sessions within concurrent web pages traffic, enabling highly detailed, per web page QoE inference in practical deployments. Extensive evaluations over a large and heterogeneous dataset composed of web and app measurements, using different device types and for top-popular websites and apps, confirm the out-performance of DeepQoE over previously used shallow-learning models, as well as the deep-model generalization to different devices, web pages, apps, and network setups. DeepQoE is the first deployable system providing such a deep, highly-detailed QoE for individual web browsing and mobile apps over encrypted traffic, using deep learning models on heterogeneous measurements.}, author = {Casas, Pedro and Wassermann, Sarah and Seufert, Michael and Wehner, Nikolas and Dinica, Olivia and Hoßfeld, Tobias}, booktitle = {Network Traffic Measurement and Analysis Conference (TMA)}, keywords = {sys:hidden:inproceedings}, month = 6, title = {X-Ray Goggles for the ISP: Improving in-Network Web and App QoE Monitoring with Deep Learning}, year = 2022 }

%0 Conference Paper %1 info3-inproceedings-2022-11 %A Casas, Pedro %A Wassermann, Sarah %A Seufert, Michael %A Wehner, Nikolas %A Dinica, Olivia %A Hoßfeld, Tobias %B Network Traffic Measurement and Analysis Conference (TMA) %D 2022 %T X-Ray Goggles for the ISP: Improving in-Network Web and App QoE Monitoring with Deep Learning %X The wide adoption of end-to-end encryption is drastically limiting the visibility Internet Service Providers (ISPs) have on the performance of the services consumed by their customers. In times of strong competition, where customer experience plays a key role in churn management, ISPs require novel solutions enabling network-wide Quality of Experience (QoE) monitoring. To this end, we present DeepQoE, a deep-learning based approach to infer the QoE of web services and mobile applications from the ISP perspective, relying exclusively on the analysis of encrypted network traffic. Using raw features derived from the encrypted stream of bytes as input to deep Convolutional Neural Networks (CNNs), DeepQoE infers the Speed Index of web browsing sessions and general mobile apps with unprecedented accuracy, improving the state of the art by more than 25%, and reducing the QoE inference error in terms of mean opinion scores by nearly 40%. DeepQoE implements a web fingerprinting solution to identify individual web browsing sessions within concurrent web pages traffic, enabling highly detailed, per web page QoE inference in practical deployments. Extensive evaluations over a large and heterogeneous dataset composed of web and app measurements, using different device types and for top-popular websites and apps, confirm the out-performance of DeepQoE over previously used shallow-learning models, as well as the deep-model generalization to different devices, web pages, apps, and network setups. DeepQoE is the first deployable system providing such a deep, highly-detailed QoE for individual web browsing and mobile apps over encrypted traffic, using deep learning models on heterogeneous measurements.

A comprehensive list of all publications can be found here: Publications

Nikolas Wehner M. Sc.

Research Interests

Recent Publications

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