Keynote Speakers

Steven Furnell
University of Nottingham, United Kingdom

The strange world of the password
Despite years of evidence of poor practice, people continue to choose weak passwords and continue to be allowed to do so. Normally, if something is broken then the answer is to fix or replace it. However, with passwords the problem seems able to persist unchecked and we continue to use them extensively despite the flaws. Adding further evidence of the issue, this presentation reports on the fifth run of a study into the provision of password guidance and the enforcement of password rules by a series of leading websites. The investigation has been conducted every 3-4 years since 2007 and the latest findings continue to reveal areas of notable weakness. This includes many sites still offering little or no meaningful guidance, and still permitting users to choose passwords that ought to be blocked at source. It seems that while we remain ready to criticise users for making poor choices, we repeatedly fail to take steps that would help them to do better.

Steven Furnell is a professor of cyber security at the University of Nottingham in the United Kingdom. He is also an Adjunct Professor with Edith Cowan University in Western Australia and an Honorary Professor with Nelson Mandela University in South Africa. His research interests include usability of security and privacy, security management and culture, and technologies for user authentication and intrusion detection. He has authored over 350 papers in refereed international journals and conference proceedings, as well as various books, book chapters and industry reports. Prof. Furnell is the UK representative to Technical Committee 11 (security and privacy) within the International Federation for Information Processing, as well as the editor-in-chief of Information and Computer Security, and a Fellow and board member of the Chartered Institute of Information Security.

Delphine Reinhardt
University of Göttingen, Germany

Usable Privacy: Retrospective and Challenges ahead
Since the introduction of the GDPR  and the resulting cookie banners, providing or not our consent to data collection has become a recurrent activity that requests attention and time for each visited website.  While consent is an important instrument to protect our privacy, its implementation is a source of annoyance for most website visitors due to its lack of usability. As a result, they may choose the easiest way and click on the most attractive button without a second thought, thus voiding the original intention beyond an informed consent. To avoid such effects for which the users are not to blame, different usable privacy solutions have been proposed in the past. In this keynote, we will consider the different steps beyond consent in which the users can be involved and detail selected examples. Based on them, we will identify future research directions and discuss challenges that we will need to solve in the next years as a community.

Prof. Dr.-Ing. Delphine Reinhardt is a full professor and head of the Computer Security and Privacy group at the University of Göttingen. She is a member of the Institute of Computer Science and the Campus Institute Data Science (CIDAS). In 2019, she was nominated as one of 10 worldwide “Rising Stars in Networking and Communications” by N2Women and was awarded the Johann-Philipp-Reis-Preis for outstanding innovative publications. Before moving to Göttingen in January 2018, she was an assistant professor at Rheinische Friedrich-Wilhelms-Universität Bonn in Germany from 2014 to 2017, leading the “Privacy and Security in Ubiquitous Computing” group at the Institute of Computer Science 4. She was also associated to the Fraunhofer Institute for Communication, Information Processing and Ergonomics (FKIE) during that time. She completed her doctoral degree in computer science (with distinction) on privacy in participatory sensing in 2013 at Technische Universität Darmstadt. Her dissertation was awarded by the Communication and Distributed Systems Group (KuVS) supported by the German Informatics Society (GI) and ITG-VDE, the Information Technology Society (ITG) of the German Association for Electrical, Electronic and Information Technologies (VDE), as well as the Association of the Friends of the Technische Universität Darmstadt for outstanding academic achievements. Since 2009, she hold a double-degree in electrical engineering from TU Darmstadt and Ecole Nationale Supérieure de l’Electronique et ses Applications (ENSEA), France.

Alexander Jung
Assistant Professor, Aalto University, Finland
Associate Editor, IEEE Signal Processing Letters

Explainable Empirical Risk Minimization
The successful application of machine learning (ML) methods becomes increasingly dependent on their interpretability or explainability. Designing explainable ML systems is instrumental to ensuring transparency of automated decision-making that targets humans. The explainability of ML methods is also an essential ingredient for trustworthy artificial intelligence. A key challenge in ensuring explainability is its dependence on the specific human user (“explainee”).
The users of machine learning methods might have vastly different background knowledge about machine learning principles. One user might have a university degree in machine learning or related fields, while another user might have never received formal training in high-school mathematics. We measure explainability via the conditional entropy of predictions, given some user signal. This user signal might be obtained from user surveys or biophysical measurements.
We propose explainable empirical risk minimization (EERM) principle of learning a hypothesis that optimally balances between the subjective explainability and risk.
The EERM principle is flexible and can be combined with arbitrary machine learning models. We present several practical implementations of EERM for linear models and decision trees. Numerical experiments demonstrate the application of EERM to detecting the use of inappropriate language on social media.

Alexander Jung received the Ph.D. degree (with sub auspiciis) in 2012 from Technical University Vienna (TU Vienna). After Post-Doctoral periods at TU Vienna and ETH Zurich, he joined Aalto University as an Assistant Professor for Machine Learning in 2015. He leads the group “Machine Learning for Big Data” that studies explainable machine learning in network-structured data. Prof. Jung first-authored a paper that won a Best Student Paper Award at IEEE ICASSP 2011. He received an AWS Machine Learning Research Award and was the “Computer Science Teacher of the Year” at Aalto University in 2018. Currently, he serves as an associate editor for the IEEE Signal Processing Letters and as the chair of the IEEE Finland Jt. Chapter on Signal Processing and Circuits and Systems. He authored the textbook, Machine Learning: The Basics (Springer, 2022).

R.G. Goebel
University of Alberta, Canada
XAI-Lab in Edmonton, Alberta, Canada

Explanation as an essential component of machine-mediated acquisition of knowledge for predictive models
Explanation is not a recent invention precipitated by black-box predictive models, but rather a revival of the role of scientific explanation as a remedy to create trust and transparency for applications of machine learning.  We note two strong trends in the grand challenge of the knowledge acquisition bottleneck, and propose that explanatory knowledge must be acquired concurrently in the process of supervised learning.   The resource costs to do so must be balanced in a tradeoff of explainability and knowledge acquisition resources, e.g., as in federated learning systems.

R.G. (Randy) Goebel is Professor of Computing Science at the University of Alberta, and head of the XAI-Lab in Edmonton, Alberta, Canada, and concurrently holds the positions of Associate Vice President Research, and Associate Vice President Academic. He is also co-founder and principle investigator in the Alberta Innovates Centre for Machine Learning. He holds B.Sc., M.Sc. and Ph.D. degrees in computer science from the University of Regina, Alberta, and British Columbia, and has held faculty appointments at the University of Waterloo, University of Tokyo, Multimedia University (Malaysia), Hokkaido University, and has worked at a variety of research institutes around the world, including DFKI (Germany), NICTA (Australia), and NII (Tokyo), was most recently Chief Scientist at Alberta Innovates Technology Futures. His research interests include applications of machine learning to systems biology, visualization, and web mining, as well as work on natural language processing, web semantics, and belief revision. He has experience working on industrial research projects in scheduling, optimization, and natural language technology applications.

Matthew E. Taylor
Director, Intelligent Robot Learning Lab, Associate Professor & Graduate Admissions Chair, Computing Science
Fellow and Fellow-in-Residence, Alberta Machine Intelligence Institute
Canada CIFAR AI Chair, Amii

Reinforcement Learning in the Real World: Challenges and Opportunities for Human-Agent Interaction
While reinforcement learning (RL) has had many successes in video games and toy domains, recent success in high-impact problems shows that this mature technology can be useful in the real world. This talk will highlight some of these successes, with an emphasis on how RL is making an impact in commercial settings, as well as what problems remain before it can become plug-and-play like many supervised learning technologies. Further, we will argue that RL, like all current AI technology, is fundamentally a human-in-the-loop paradigm. This framing will help motivate why additional fundamental research at the interaction of humans and RL agents is critical to helping RL move out of the lab and into the hands of non-academic practitioners.

Matt Taylor is an Associate Professor of Computing Science at the University of Alberta, where he directs the Intelligent Robot Learning Lab. He is also a Fellow and Fellow-in-Residence at Amii (the Alberta Machine Intelligence Institute). His current research interests include fundamental improvements to reinforcement learning, applying reinforcement learning to real-world problems, and human-AI interaction. His book “Reinforcement Learning Applications for Real-World Data” by Osborne, Singh, and Taylor is aimed at practitioners without degrees in machine learning and has an expected release date of Summer 2022.

Fabio Di Franco
ENISA, Greece

Cybersecurity Skills Gap: ENISA Analysis and Actions
Fabio will provide a holistic view on the nature and characteristics of the skills gap in Europe and the results of the joint effort done with other EU players (eg. the pilots of the EU Competence Network). He will report on the European Cybersecurity Skills Framework (ECSF) which aims to close the cybersecurity skills’ gap on the European labour market, building comprehensive bridges between European workplace context and learning environment through an EU skills framework.  He will also provide insights on the cybersecurity higher education database (CyberHEAD), an initiative to allow young talents to make informed decisions on the variety of possibilities offered by higher education in cybersecurity through an easy-to-use web portal.

Fabio Di Franco is currently leading the activities in ENISA on cyber skills development for highly skilled people. He is also responsible for developing and delivering trainings to EU member states and EU institutions on information security management and IT security. Fabio has a PhD in telecommunication engineering and is a Certified Information Systems Security Professional (CISSP).

Dr. Yulia Cherdantseva
Senior Lecturer at the School of Computer Science & Informatics at Cardiff University, United Kingdom

CyBOK – The Cyber Security Body Of Knowledge
Cyber Security Body of Knowledge (CyBOK) is a major project sponsored by the UK National Cyber Security Centre with the aim of developing a substantial resource offering a guide to the Cyber Security as a discipline and as a field of professional practice.  CyBOK codifies the foundational knowledge in cyber security for education and professional training.  It is an open and freely accessible resource (www.cybok.org) developed by the Community for the Community with contributions from over 115 experts across the world since 2017. CyBOK v1.1 is constituted by 21 knowledge areas. There are also free supplementary resources for students, educators and trainers, e.g. podcasts, resources for developing programmes based on CyBOK, lab materials, case studies for use in classroom, etc. This presentation will describe the process of developing CyBOK and maintaining it up to date, discuss the role of the international community in this process, outline the use cases of CyBOK and the future directions of the CyBOK project evolution.

Dr. Yulia Cherdantseva is a Senior Lecturer at the School of Computer Science & Informatics at Cardiff University. Yulia worked as a lead researcher on the project “Supervisory Control and Data Acquisition Systems Cyber Security Lifecycle (SCADA-CSL)” funded by the Airbus Group Endeavr Wales and the Welsh Assembly Government, where she developed a novel SCADA Cyber Security, Safety and Risk (SCADA CSSR) graphical extension for BPMN 2.0 and a configurable dependency model of a SCADA system. In 2020-2021, she led an NCSC and RISCS funded project about cyber-security decision-making by SMEs which resulted in the development of the Best Practice Guide for SME in Cyber Security Investment Decision-Making. In 2021, she was awarded an EPSRC grant for developing a framework for risk-informed and metrics-enriched cybersecurity playbooks for enhancing CNI resilience. Yulia is a cyber skills lead at the School and is interested in cybersecurity education from the primary school up to professional development level. From May 2021, Yulia is a member of the CyBOK Executive Board. Yulia is passionate about equality and diversity in cybersecurity – she is a member of the CIISec’s Steering Committee “Women in Cyber” and of the CREST’s working group on Inclusion and Diversity.

Dr. Xiaolu Hou
Faculty of Informatics and Information Technologies, Slovak University of Technology, Slovakia

Artificial Intelligence-Assisted Side Channel Attacks
Deep neural networks (DNN) have gained popularity in the last decade due to advances in available computational resources. In particular, side-channel attacks (SCA) have received the most attention as being a classification problem, DNN comes as a natural candidate. In this talk, we will first provide the basics of SCA and explain how it can recover the secret key of a cryptographic implementation. Then, we will present the recent literature on applications of DNN to SCA. As a demonstration, we will detail a work that aims to propose a general framework that helps users with the overall trace analysis aided by DNN, minimizing the necessity for architecture adjustments by the user.

Dr. Xiaolu Hou is currently an Assistant Professor at Slovak University of Technology. She received her Ph.D. degree in Mathematics from Nanyang Technological University, Singapore, in 2017. Her current research focus is on fault injection and side-channel attacks on both cryptographic implementations and neural networks. She also has research experience in AI-assisted cryptanalysis, location privacy, and multiparty computation.

Assoc. prof. Gabriele Costa
IMT School for Advanced Studies Lucca, Italy
https://www.imtlucca.it/en

Security-by-Design in Intelligent Infrastructures: the HAII-T orchestrator
In the last years Security-by-Design has emerged as the main methodology for securing the life cycle of software and systems. Its effectiveness is the result of a strong integration with all the development phases, from the earliest conceptualization and design to the final disposal. Large scale, critical infrastructures can benefit the most from this approach. Nevertheless, they also carry an extreme degree of complexity that must be dealt with. In this talk we will consider the SPARTA perspective on the definition and implementation of a secure orchestrator for making intelligent infrastructures Secure-by-Design.

Gabriele Costa is associate professor at the SySMA Group of the IMT School for Advanced Studies. He received his M.Sc. in Computer Science in 2007 and his Ph.D. in Computer Science in 2011, both at the University of Pisa. He was a member of the cybersecurity group of the Istituto di Informatica e Telematica (IIT) of the CNR. His appointments include a period as visiting researcher at ETH Zurich in 2016-2017. He was co-founder of the Computer Security Laboratory (CSec) at DIBRIS (Computer Science and Computer Engineering Department of the University of Genoa). He is co-founder and CRO of Talos, a spin-off of DIBRIS focused on Cybersecurity. His main focus is on studying and applying formal methods for the automatic verification and security testing of mobile and modular systems .