Research

My primary research interest lies in computer networks (mostly in network traffic measurements and cybersecurity) and in machine learning.

During my PhD, my goal was to deliver algorithms and software systems to efficiently measure video and web QoE in today's encrypted Internet. I was supervised by Dr. Pedro Casas (AIT Austria) and Prof. Tanja Zseby (TU Wien). As I personally see it, there is today an ever-growing need for better approaches to enhance the functioning of Internet-like networks and services that are commonly consumed by end-users, and this is why I am deeply interested in QoE. Indeed, today, we are accomplishing more and more tasks via the Internet and it is thus crucial that we can do so without being hindered by poor Internet performance. Questions like "Why does the video I am watching on YouTube keep stalling?", "Why is the website of my favourite newspaper so slow to load today?" or "Why is the audio quality of my Skype call so bad?" are unfortunately asked very (and even too) frequently.

During my MSc. studies, I conducted research in the field of network measurements under the supervision of Dr. Pedro Casas and Prof. Dr. Benoit Donnet. In particular, I worked on Internet path dynamics and performance, machine learning for networking, anycast in cellular networks, and malware detection in smartphones. My Master's thesis is entitled Anycast-based DNS in Mobile Networks and I carried out this project under the supervision of Prof. Fabián Bustamante and Prof. Benoit Donnet.

In the context of my research, I have developed three open-source tools, namely RAL, reinforced stream-based active learning, NETPerfTrace, an Internet path tracking system, and DisNETPerf, an Internet paths performance analyzer. I also contributed to the well-known Paris Traceroute extension to the standard traceroute tool.

RAL

Reinforced stream-based active learning. RAL is an active-learning technique relying on reinforcement-learning principles, using rewards and bandit algorithms for efficiently retrieving valuable but expensive ground-truth data. GitHub repo of RAL.

NETPerfTrace

An Internet path tracking system. NETPerfTrace is a tool capable of forecasting path changes and path latency variations. For more information about this tool, please have a look at my papers or visit the GitHub repo of NETPerfTrace.

DisNETPerf

A distributed Internet paths performance analyzer developed in the context of my research internship at FTW Vienna in 2015. For more information about this work, please have a look at my papers or visit the GitHub repo of DisNETPerf.

libparistraceroute

During my internship at LiP6 in Paris in 2014, I implemented a generic ping tool based on libparistraceroute which can handle IPv4, IPv6 and TCP, UDP, ICMP probes. I also extended the library itself. More information can be found on the project's GitHub repo of libparistraceroute.

Publications

Theses:

Machine Learning for Network Traffic Monitoring and Analysis: Application to Internet QoE Assessment and Network Security
S. Wassermann
PhD thesis, Vienna University of Technology, 2022

Anycast-based DNS in Mobile Networks
S. Wassermann
MSc. thesis, University of Liège, 2017

Journal papers:

Adaptive and Reinforcement Learning Approaches for Online Network Monitoring and Analysis
S. Wassermann, T. Cuvelier, P. Mulinka, P. Casas
in IEEE Transactions on Network and Service Management (TNSM), vol. 18, no. 2, pp. 1832-1849, 2021

ViCrypt to the Rescue: Real-time, Machine Learning-driven Video QoE Monitoring for Encrypted Streaming Traffic
S. Wassermann, M. Seufert, P. Casas, L. Gang, K. Li
in IEEE Transactions on Network and Service Management (TNSM), vol. 17, no. 4, pp. 2007-2023, 2020

Considering User Behavior in the Quality of Experience Cycle: Towards Proactive QoE-aware Traffic Management
M. Seufert, S. Wassermann, P. Casas
in IEEE Communications Letters, vol. 23, no. 7, pp. 1145-1148, 2019

Unveiling Network and Service Performance Degradation in the Wild with mPlane
P. Casas, P. Fiadino, S. Wassermann, S. Traverso, A. D'Alconzo, E. Tego, F. Matera, M. Mellia
in IEEE Communications Magazine, Network Testing Series, vol. 54, no. 3, pp. 71-79, 2016

Conference papers:

Fingerprinting Webpages and Smartphone Apps from Encrypted Network Traffic with WebScanner
P. Casas, N. Wehner, S. Wassermann, M. Seufert
in 27th IEEE Global Internet (GI) Symposium, Paris, France, 2022

Not all Web Pages are Born the Same. Content Tailored Learning for Web QoE Inference
P. Casas, S. Wassermann, N. Wehner, M. Seufert, T. Hossfeld
in 6th IEEE International Symposium on Measurements & Networking (M&N), Padua, Italy, 2022

X-Ray Goggles for the ISP: Improving in-Network Web and App QoE Monitoring with Deep Learning
P. Casas, S. Wassermann, M. Seufert, N. Wehner, O. Dinica, T. Hossfeld
in 6th IFIP Network Traffic Measurement and Analysis Conference (TMA), Enschede, The Netherlands, 2022

DeepCrypt – Deep Learning for QoE Monitoring and Fingerprinting of User Actions in Adaptive Video Streaming
P. Casas, M. Seufert, S. Wassermann, B. Gardlo, N. Wehner, R. Schatz
in 8th IEEE International Conference on Network Softwarization (NetSoft), Milan, Italy, 2022

Mobile Web and App QoE Monitoring for ISPs - from Encrypted Traffic to Speed Index through Machine Learning
P. Casas, S. Wassermann, N. Wehner, M. Seufert, J. Schüler, T. Hossfeld
in 13th IFIP Wireless and Mobile Networking Conference (WMNC), virtual, 2021
Best Paper Award

Are you on Mobile or Desktop? On the Impact of End-User Device on Web QoE Inference from Encrypted Traffic
S. Wassermann, P. Casas, Z. Ben Houidi, A. Huet, M. Seufert, N. Wehner, J. Schüler, S. Cai, H. Shi, J. Xu, T. Hoßfeld, D. Rossi
in 16th International Conference on Network and Service Management (CNSM), virtual, 2020

ADAM & RAL: Adaptive Memory Learning and Reinforcement Active Learning for Network Monitoring
S. Wassermann, T. Cuvelier, P. Mulinka, P. Casas
in 15th International Conference on Network and Service Management (CNSM), Halifax, Canada, 2019
Fast-tracked to IEEE Transactions on Network and Service Management (TNSM)

On the Analysis of YouTube QoE in Cellular Networks through in-Smartphone Measurements
S. Wassermann, P. Casas, M. Seufert, F. Wamser
in 12th IFIP Wireless and Mobile Networking Conference (WMNC), Paris, France, 2019
Best Paper Award runner up

Beauty is in the Eye of the Smartphone Holder – A Data Driven Analysis of YouTube Mobile QoE
N. Wehner, S. Wassermann, P. Casas, M. Seufert, F. Wamser
in 14th International Conference on Network and Service Management (CNSM), Rome, Italy, 2018

Anycast on the Move: A Look at Mobile Anycast Performance
S. Wassermann, J. P. Rula, F. E. Bustamante, P. Casas
in Network Traffic Measurement and Analysis Conference (TMA) 2018, Vienna, Austria, 2018

Improving QoE Prediction in Mobile Video through Machine Learning
P. Casas, S. Wassermann
in 8th International Conference on Network of the Future (NoF), London, United Kingdom, 2017
Best Paper Award candidate

Workshop papers:

Improving Web QoE Monitoring for Encrypted Network Traffic through Time Series Modeling
N. Wehner, M. Seufert, J. Schüler, S. Wassermann, P. Casas, T. Hoßfeld
in IFIP Performance 2020 Workshops, Workshop on AI in Networks (WAIN), virtual, 2020

I See What you See: Real Time Prediction of Video Quality from Encrypted Streaming Traffic
S. Wassermann, M. Seufert, P. Casas, L. Gang, K. Li
in 4th ACM MOBICOM Workshop on QoE-based Analysis and Management of Data Communication Networks (Internet-QoE), Los Cabos, Mexico, 2019

RAL – Improving Stream-Based Active Learning by Reinforcement Learning
S. Wassermann, T. Cuvelier, P. Casas
in European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), Workshop on Interactive Adaptive Learning (IAL), Würzburg, Germany, 2019

Remember the Good, Forget the Bad, do it Fast: Continuous Learning over Streaming Data
P. Mulinka, S. Wassermann, G. Marín, P. Casas
in Continual Learning Workshop at NeurIPS 2018, Montreal, Canada, 2018

Machine Learning Models for YouTube QoE and User Engagement Prediction in Smartphones
S. Wassermann, N. Wehner, P. Casas
in IFIP Performance 2018 Workshops, Workshop on AI in Networks (WAIN) 2018, Toulouse, France, 2018

BIGMOMAL - Big Data Analytics for Mobile Malware Detection
S. Wassermann, P. Casas
in ACM SIGCOMM 2018 Workshop on Traffic Measurements for Cybersecurity (WTMC), Budapest, Hungary, 2018

NETPerfTrace - Predicting Internet Path Dynamics and Performance with Machine Learning
S. Wassermann, P. Casas, T. Cuvelier, B. Donnet
in ACM SIGCOMM 2017 Workshop on Big Data Analytics and Machine Learning for Data Communication (Big-DAMA), Los Angeles (CA), USA, 2017

On the Analysis of Internet Paths with DisNETPerf, a Distributed Paths Performance Analyzer
S. Wassermann, P. Casas, B. Donnet, G. Leduc, M. Mellia
in IEEE WNM, Dubai, United Arab Emirates, 2016

Extended abstracts:

How Good is your Mobile (Web) Surfing? Speed Index Inference from Encrypted Traffic
S.Wassermann, P. Casas, M. Seufert, N. Wehner, J. Schüler, T. Hossfeld
in ACM SIGCOMM 2020 Posters, Demos, and Student Research Competition, virtual, 2020

RAL – Reinforcement Active Learning for Network Traffic Monitoring and Analysis
S.Wassermann, T. Cuvelier, P. Casas
in ACM SIGCOMM 2020 Posters, Demos, and Student Research Competition, virtual, 2020

Machine Learning based Prediction of Internet Path Dynamics
S.Wassermann, P. Casas, B. Donnet
in ACM CoNEXT Student Workshop, Irvine (CA), USA, 2016

Towards DisNETPerf: a Distributed Internet Paths Performance Analyzer
S. Wassermann, P. Casas, B. Donnet
in ACM CoNEXT Student Workshop, Heidelberg, Germany, 2015

Demo sessions:

Let me Decrypt your Beauty: Real-time Prediction of Video Resolution and Bitrate for Encrypted Video Streaming
S. Wassermann, M. Seufert, P. Casas, L. Gang, K. Li
in Demonstrations of the Network Traffic Measurement and Analysis Conference (TMA) 2019, Paris, France, 2019

Distributed Internet Paths Performance Analysis through Machine Learning
S. Wassermann, P. Casas
in Demonstrations of the Network Traffic Measurement and Analysis Conference (TMA) 2018, Vienna, Austria, 2018
Best Demo Award candidate

Reverse Traceroute with DisNETPerf, a Distributed Internet Paths Performance Analyzer
S. Wassermann, P. Casas
in Demonstrations of the 41th Annual IEEE Conference on Local Computer Networks (LCN-Demos 2016), Dubai, United Arab Emirates, 2016

Posters:

Improving Stream-Based Active Learning with Reinforcement Learning
S.Wassermann, T. Cuvelier, P. Casas
accepted to the poster session at the Women in Machine Learning (WiML) Workshop co-located with NeurIPS, Vancouver, Canada, 2019

Decrypting Video Quality from Encrypted Streaming Traffic
S.Wassermann, P. Casas
accepted to the poster session at the Women in Machine Learning (WiML) Workshop co-located with NeurIPS, Vancouver, Canada, 2019

ViCrypt: Real-time, Fine-grained Prediction of Video Quality from Encrypted Streaming Traffic
S. Wassermann, M. Seufert, P. Casas
presented during the poster session at the ACM Internet Measurement Conference (IMC), Early Work, Tools, and Datasets Track, Amsterdam, Netherlands, 2019

BIGMOMAL – Big Data Analytics for Mobile Malware Detection
S.Wassermann, P. Casas
presented during the poster session at the ACM Internet Measurement Conference (IMC), London, United Kingdom, 2017

Anycast on the Move – A First Look at Mobile Anycast Performance
S. Wassermann, J. P. Rula, F. E. Bustamante
presented during the poster session at the ACM Internet Measurement Conference (IMC), London, United Kingdom, 2017

Technical reports:

Predicting Internet Path Dynamics and Performance with Machine Learning
S. Wassermann, P. Casas, T. Cuvelier, B. Donnet
AIT-Big-DAMA Tech. Rep. A3215, 2017

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