DAY 2 - Invited Speakers per track/session


Thomas Hupperich

Thomas Hupperich is assistant professor at the University of Twente, Enschede, NL. His main research topics of IT security include system fingerprinting, user tracking, authentication and privacy – especially of mobile devices. Further fields of interest are economic and social factors of IT security. He graduated at the chair for systems security at Ruhr-University, Bochum, Germany.






Title: Digital Fingerprinting of Devices for System Recognition

Client fingerprinting techniques enhance classical cookie-based recognition mechanisms in the domain of web-based user tracking.
A unique identifier is created based on characteristic attributes of the client device and then used for deployment of personalized advertisements, price differentiation or fraud detection.
Such identifiers can also be used in authentication scenarios as a user's or device's fingerprint.
Whereas fingerprinting performs well for highly customized devices, these methods often lack in precision for highly standardized devices like mobile phones.
Depending on the use case, the development of tailor-made recognition mechanisms is required.
Fingerprinting of web clients is often seen as an offence to web users' privacy as it usually takes place without the users' knowledge, awareness, and consent.


Veelasha Moonsamy

Veelasha Moonsamy is an Assistant Professor at Utrecht University (The Netherlands). She was a postdoctoral researcher in the Digital Security group at Radboud University (The Netherlands) from 2015-2017. Veelasha obtained her PhD from Deakin University in Melbourne (Australia), under the supervision of Prof. Lynn Batten in 2015. Her research interests revolves around security and privacy on mobile devices, in particular side- and covert-channel attacks, malware detection and mitigation of information leaks at application and hardware level.


Title: "No Free Charge Theorem: a Covert Channel via USB Charging Cable on Mobile Devices"

Side-channel attacks on mobile devices have gained increasing attention since their introduction in 2007. While traditional side-channel attacks, such as power analysis attacks and electromagnetic analysis attacks, required physical presence of the attacker as well as expensive equipment, an (unprivileged) application is all it takes to exploit the leaking information on modern mobile devices. Given the vast amount of sensitive information that are stored on smartphones, the ramifications of side-channel attacks affect both the security and privacy of users and their devices.

I will present our latest work on how an adversary can exploit side-channel information, in this case power from the phone battery, to maliciously control a public charging station in order to exfiltrate data from a smartphone via a USB charging cable (i.e. without using the data transfer functionality).


Kouichi Sakurai

Kouichi Sakurai received the B.S. degree in mathematics from the Faculty of Science, Kyushu University in 1986. 

He received the M.S. degree in applied science in 1988, and the Doctorate in engineering in 1993 from the Faculty of Engineering, Kyushu University. He was engaged in research and development on cryptography and information security at the Computer and Information Systems Laboratory at Mitsubishi Electric Corporation from 1988 to 1994. From 1994, he worked for the Dept. of Computer Science of Kyushu University in the capacity of associate professor, and became a full professor there in 2002. And concurrently he is working also with CyberSecurity Center of Kyushu University, and doing joint project with IIT-Delhi on IoT-security. Professor Sakurai has published more than 350 academic papers around cryptography, information security, and CyberSecurity (See his DBLP ).



Title : Power and limitation of Adversarial Machine Learning and their consequences

Recent research has revealed that the output of Deep Neural Networks (DNN) can be easily altered by modifying small perturbations to the input vector.  Also we have shown an attack in an extremely limited scenario where only one pixel can be altered, and succeed to generate one-pixel adversarial perturbations based on differential evolution.  Thus, our attack explores a different take on adversarial machine learning in an extreme limited scenario, showing that current DNNs are also vulnerable to such low dimension attacks. This talk discusses practical consequences from these observation on security and privacy related to Machine learning based applications.

# A part of this research is joint with Dr. Danilo Vargas and Mr. JiaWei Su of Kyushu Univ.



Peter Bosman

Dr. Peter A.N. Bosman is a senior researcher heading the Medical Informatics subgroup of the Life Sciences and Health research group at the Centrum Wiskunde & Informatica (CWI) (Center for Mathematics and Computer Science) located in Amsterdam, the Netherlands. Dr. Bosman was formerly affiliated with Utrecht University, where he also obtained his M.Sc. and Ph.D. degrees in Computer Science. He has (co-)authored over 100 refereed publications, out of which 4 received best paper awards.

Dr. Bosman's fundamental research focus is on the design and application of efficiently scalable model-based evolutionary algorithms (EAs) for single- and multi-objective optimization, and machine learning. Dr. Bosman's applied research focus is on solving key problems in the Life-Science and Health (LSH) domain that require optimization and/or machine learning. A specific focus thereby is on radiation oncology, including automated treatment planning, 3D dose reconstruction, and deformable image registration.



Title: Evolutionary Intelligence in Medicine

Artificial Intelligence (AI) increasingly pervades the daily news, with self-driving cars, robots, and face recognition even on smart phones. In this talk, I will focus on a particular subfield of AI: that of Evolutionary Algorithms (EAs). In particular, I will introduce the state-of-the-art Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) family that automatically determines how to best combine solutions during optimization. I will illustrate the advantageous properties of GOMEAs over classical ``blind'' EAs, showing how GOMEAs are capable of obtaining (near-)optimal results for problems with millions of variables in less than an hour on a normal desktop computer, whereas classical EAs can only do this for tens or a few hundred variables. I will then discuss some of the projects my group is currently involved in, outlining how EAs in general, and GOMEAs in particular, are used to tackle real-world optimization problems in the medical domain so as to innovate clinical practice, in collaboration with hospitals, academic partners, and industry partners.


Wessel Kraaij

Wessel Kraaij joined the Leiden Institute of Advanced Computer Science (LIACS) as professor of Applied Data Analytics in March 2016 and leads the university wide Data Science Research Programme since March 2018. He is also a principal scientist at TNO. Last year, Kraaij was appointed ACM Distinguished Member for his significant contribution in the field of Information Retrieval. As a specialist in extracting valuable information from unstructured data, he combines behavioral sciences with computational science. Since a few years Kraaij has been interested in  the potential of personal health/disease trajectories for personalized health advice.  This domain involves data science but also data governance challenges.


Internet of Things

Wilco Bonestroo Wilco Bonestroo is associate professor (associate lector) in the Ambient Intelligence research group at Saxion University of Applied Sciences ( In his research, Wilco applies traditional software engineering practices on the development of embedded systems. Important topics in his research are 'Internet of Things' and, more specifically, localisation or positions in IoT. Knowledge of the location of a thing or a person is important in many systems.


Title: Location of Things (and People)


Kallol Das

Dr. Kallol Das is a research scientist at the Netherlands Organization for Applied Scientific Research (TNO). He received his PhD in computer science from the University of Twente and MSc in electrical engineering from Chalmers University of Technology. His research interests include industrial and heterogeneous wireless networks, the internet of things, and wireless localization. Dr. Das has been involved in several international research projects including the recently started EU Horizon 2020 Clear5G project.



Title: 5G for Factories of the Future


Gerd Kortuem

Prof. Gerd Kortuem is Professor of Internet of Things at the Design Engineering department at Delft University of Technology, and principle investigator at the Amsterdam Institute for Advanced Metropolitan Solutions.  His research focuses on the Internet of Things as new material for design and explores the design of connected products and services for a sustainable future. In 2002 he completed his PhD in the field of wearable computing and has since then investigated  IoT technologies for smart workplaces, smart cities, public transportation, urban energy with companies such as BP, BT, E.On, Tech Mahindra and Alliander.



Title: The Internet of Things as Creative Design Material


Fernando Kuipers

Fernando A. Kuipers is an associate professor and head of the Lab on Internet Science at Delft University of Technology (TU Delft). In 2004, he obtained his Ph.D. degree cum laude, the highest possible distinction at TU Delft. His research focus is on network optimization, network resilience, Quality of Service, and Quality of Experience and addresses problems in software-defined networking, Internet-of-Things, optical networking, content distribution, and critical infrastructures. His work on these subjects include distinguished papers at IEEE INFOCOM 2003, Chinacom 2006, IFIP Networking 2008, IEEE FMN 2008, IEEE ISM 2008, ITC 2009, IEEE JISIC 2014, NetGames 2015, and EuroGP 2017.
Fernando Kuipers is senior member of the IEEE, was a visiting scholar at Technion – Israel Institute of Technology (in 2009) and Columbia University in the City of New York (in 2016), and is board member of the IEEE Benelux chapter on communications and vehicular technology, the Royal Netherlands Society of Engineers (KIVI), section Telecommunication, and the program advisory board of SURFnet on connecting infrastructures.



Titel: Go Long … Range

The radio technology LoRa enables long-range low-power communication for IoT applications. Since the specification of the corresponding MAC protocol LoRaWAN in 2015, the adoption of LoRa technology has taken flight. This talk will provide insight into several performance and security aspects of LoRa(WAN).


Fatjon Seraj

Dr. Fatjon Seraj is an R&D enthusiast with a particular interest in Machine Learning algorithms and Crowd-based data generation. He received his Phd degree in June 2017 and was awarded the PhD award of the World Class Maintenance (WCM) for his doctoral thesis entitled "Rolling vibes: continuous transport infrastructure monitoring” in October 2017.



Title: Road safety estimation: A crowd-based approach
Road safety is one the main concerns for both road management authorities and the road users. Although the quality of the road can be satisfactory from the engineering point of view they can yet pose danger to the road users when a combination of higher speed limits and road geometry occurs. The recent technological developments gave rise to new opportunities to
investigate di erent parameters of the transport infrastructure. Crowd based sourcing, sensing and computing can be performed using ubiquitous devices like smartphones. Smartphones can perform sophisticated machine learning based algorithms and their highly connected features makes them the best candidates for these tasks. Considering the majority of the drivers will obey the traffic rules we can collect important information regarding the safety attributes of the road segments. Smartphones are equipped with inertial sensors such accelerometers that measure the acceleration on three axis and gyroscopes that measure rotation rate around three axis. The GPS chip inside the smartphones provides location and vehicle speed. Whenever the smartphone is mounted on the windshield, image processing from the smartphone camera is also possible.
This information can be precious when put into the context of long term and wide participant based monitoring. Sophisticated machine learning techniques allows us to recognize the dangerous spots on the road.


Janine Swaak

Dr. Janine Swaak is business developer at the foundation Twente47. The aim of Twente47 is to accelerate IoT product development. To realize this Twente47 brings together needs from industry and goverment, scientific knowlege and (start-up) IoT companies. With her academic background and ample experience in industrial and governmental organisations, Janine Swaak is able to help bridge the gap between scientific knowlege & technology and IoT business.



Title: Internet of things business opportunities: from opportunity to business


Smart Industries

Jonas Dorißen

Jonas Dorißen M.Sc., studied mechanical engineering and production engineering at the RWTH Aachen University. Since 2016 he is working as a research fellow in the department of Production Quality and Metrology at the Fraunhofer Institute for Production Technology IPT in Aachen, Germany. His field of work is the optimization of processes, with special focus on data analytics in production processes. Together with the University of Twente, he is working in the Fraunhofer Project Center on the “Industrie 4.0” audit projects in the Netherlands.


Title: Industry 4.0: Adaptive Connected Production


Jan Post

Over a period of more than 30 years Jan Post was involved in the co-operation of Philips and the academic/HBO research domain for initiating new research (bachelor/master/PhD), the guidance of students and the valorization of that research at Philips. This activity led to more than 150 publications/presentations on National and international conferences. In his current position, he is responsible for the strategic co-operation of Philips personal Care, including the Public Private Partnerships and academic research. He is responsible for the Smart Industry Field lab “Region of Smart Factory” and the HTSM roadmap Smart Industry and figurehead of the NWA-Smart Industry. He is partime professor at The university Groningen in the field of “Digital Fabrication”.


Egbert-Jan Sol

Prof Dr Ir Egbert-Jan Sol (1956) is board member of TNO Industry, program director at the Dutch Smart Industry program and 1d/w professor of Research Management at the science department of the Radboud University in Nijmegen.

Egbert-Jan Sol has a Master and Doctor degree in Mechanical Engineering from the TU/e. He started his industrial career starting as robotics system architect, became project manager (Hoogovens), development manager (Philips) and managing director of a 500 person R&D centre of Ericsson in the Netherlands (Rijen) before joining TNO in 2003. From 1990 till 1998 he was part time professor at Technology Management on Manufacturing Automation at the Eindhoven University of Technology.



Title: Smart Industry: a HTSM/ICT/NWA roadmap

Smart Industry deals with the acceleration of digitalization in industry. Internationally it is also called 4IR, the fourth industrial revolution. With the Dutch Smart Industry program we want to create in the Netherlands by 2021 the best digital and interconnected manufacturing eco-system.  

Together with the implementation plan 2018-2021 of the Smart Industry program, a jointed HTSM/ICT/NWA roadmap update 2018 was recently published.

The implementation plan deals with 8 transformation to be realized within factories: smart products, servitisation, digital factory, connected factories, sustainable factory, smart working, advanced manufacturing and flexible production, all made possible due to progress in digitalization, connectivity and new technologies. The roadmap deals with knowledge challenges as enabling technologies from mechatronics and robotics, via ICT technologies as digital twinning, data sharing, cyber security etc. to social aspects as human technology interaction and smart (societal) response. Different ICT aspects as machine learning, blockchains etc are all included. The question we are now facing is to determine which research project to solve knowledge challenges industry is willing to pay for and which will help them most in realizing their transformations.  


Biba Visnjicki

Over the last decade dr. Biba Visnjicki has supported companies world-wide in their strategy, business development and was frequently building and steering national and international innovation teams.

She started her professional career in Serbia as a product and business developer. In 1998 she become regional Managing Director of Mobile Oil for the former Yugoslavia region. After finishing her PhD studies in The Netherlands, she has focused on developing her competencies and skills in the domains of strategy, business development, analytics and methodologies and tools for effective realisation of strategy and innovation. She has achieved QFD Black Belt status from the QFD Institute of America. 

Since beginning of 2017 dr. Visnjicki, as Director Business Development, is responsible for the positioning and development of the Fraunhofer Project Center at the UT with a strong focus on Industry 4.0. solutions.


Title: Industry 4.0: Adaptive Connected Production