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Keynote Lectures

Leveraging Blockchain Technology to Enhance Security and Privacy in the Internet of Things
Sokratis K. Katsikas, Dept. of Information Security and Communication Technology, Department of Information Security and Communication Technology, Norwegian University of Science and Technology, Gjøvik, Norway

Jump, Crawl, Attract, Propagate: Security Challenges in Emerging Communication Networks
Stefan Schmid, University of Vienna, Austria


 

Leveraging Blockchain Technology to Enhance Security and Privacy in the Internet of Things

Sokratis K. Katsikas
Dept. of Information Security and Communication Technology, Department of Information Security and Communication Technology, Norwegian University of Science and Technology, Gjøvik
Norway
https://www.ntnu.edu/employees/sokratis.katsikas
 

Brief Bio
Sokratis K. Katsikas was born in Athens, Greece, in 1960. He is the Director of the Norwegian Centre for Cybersecurity in Critical Sectors and Professor with the Department of Information Security and Communication Technology, Norwegian University of Science and Technology, Norway. He is also Professor Emeritus of the Department of Digital Systems, University of Piraeus, Greece. In 2019 we was awarded a Doctorate Honoris Causa from the Department of Production and Management Engineering, Democritus University of Thrace, Greece. In 2021 he was ranked 7th in the security professionals category of the IFSEC Global influencers in security and fire list.  Among others, he has been the Rector of the Open University of Cyprus; Rector and Vice Rector of the University of the Aegean, Greece; General Secretary of Telecommunications and Posts of the Hellenic Government; Chair of the National Council of Education of Greece; member of the Board of the Hellenic Authority for the Security and Privacy of Communications; chair of the Steering Committee of the EUA Institutional Evaluation Programme; and member of the Board of the Hellenic Authority for the Quality and Accreditation of Higher Education. He chairs the Steering Committee of the ESORICS Conference and he is the Editor-in-Chief of the International Journal of Information Security.


Abstract
The Internet of Things (IoT) and Blockchain are two technologies that have dramatically changed the traditional computing models. While the IoT has enabled multiple new computing applications, it has also raised significant issues regarding security and privacy, as lightweight devices with limited resources, scattered in terms of network topology and too diverse in terms of hardware and software are nowadays used for processing huge amounts of data, including sensitive data. On the other hand, blockchain technology enables the development of secure decentralized systems and offers guarantees regarding data integrity, application logic integrity and service availability. Thus, the idea to explore the potential of employing Blockchain technology to solve some of the main security and privacy issues encountered in the IoT it seems promising. In this talk we will discuss the convergence of the two technologies, we will analyze use cases of blockchain in the IoT and the encountered difficulties therein, and we will discuss possible research directions towards enhancing the applicability of blockchain technology to the IoT domain.



 

 

Jump, Crawl, Attract, Propagate: Security Challenges in Emerging Communication Networks

Stefan Schmid
University of Vienna
Austria
 

Brief Bio
Stefan Schmid is a Professor at the Faculty of Computer Science, at University of Vienna, Austria. He obtained his diploma (MSc) in Computer Science at ETH Zurich in Switzerland (minor: micro/macro economics, internship: CERN) and did his PhD in the Distributed Computing Group led by Prof. Roger Wattenhofer, also at ETH Zurich. As a postdoc, he worked with Prof. Christian Scheideler at the Chair for Efficient Algorithms at the Technical University of Munich and at the Chair for Theory of Distributed Systems at the University of Paderborn, in Germany. From 2009 to 2015, Stefan Schmid was a senior research scientist at the Telekom Innovation Laboratories (T-Labs) and at TU Berlin in Germany (Internet Network Architectures group headed by Prof. Anja Feldmann). In 2013/14, he was an INP Visiting Professor at CNRS (LAAS), Toulouse, France, and in 2014, a Visiting Professor at Université catholique de Louvain (UCL), Louvain-la-Neuve, Belgium. From 2015 to 2017, Stefan Schmid was a (tenured) Associate Professor in the Distributed, Embedded and Intelligent Systems group at Aalborg University, Denmark, and continued working part-time at TU Berlin, Germany. Since 2015, he serves as the Editor of the Distributed Computing Column of the Bulletin of the European Association of Theoretical Computer Science (BEATCS), since 2016 as Associate Editor of IEEE Transactions on Network and Service Management (TNSM), and since 2019 as Editor of IEEE/ACM Transactions on Networking (ToN). Stefan Schmid received the IEEE Communications Society ITC Early Career Award 2016. His research interests revolve around the fundamental and algorithmic problems of networked and distributed systems.


Abstract
Communication networks have become a critical infrastructure of our digital society. But how much can we trust our networks today?
Over the last years, we have witnessed the emergence of interesting new kinds of networks. For example, programmable and virtualized networks introduced unprecedented operational flexibilities in datacenter and wide-area networks. Another example are payment channel networks which are considered a promising solution to the scalability problems of cryptocurrencies such as Bitcoin.
In this talk, I will provide an overview of the novel opportunities and challenges of emerging networking paradigms, in terms of security. In particular, I will show how such networks can increase the attack surface, enabling new attacks such as teleportation, tuple space explosion, exfiltration or denial-of-service on payments. I will then also discuss first ideas for solutions, from efficient isolation mechanisms, algorithmic networking monitoring, to machine learning.



 



 


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