Professor (Full) Aggelos Kiayias

(University of Edinburgh, U.K. & IOHK)

Title:

The Promise of Decentralization – Cyber-Security Myth or Revolution?

Abstract:

The advent of blockchain technology brought forth the property of decentralization with a promise of higher resilience to attacks, as well as increased trustworthiness and dependability for the information technology systems that are organized in a decentralized way. In this talk I will explore the concept of decentralization and overview a number of pitfalls and shortcomings in the way it can be achieved by blockchain as well as information technology and communications systems in general. Lessons learned and directions for future research will be also presented.

Bio:

Aggelos Kiayias FRSE is chair in Cyber Security and Privacy and director of the Blockchain Technology Laboratory at the University of Edinburgh. He is also the Chief Scientist at blockchain technology company IOHK. His research interests are in computer security, information security, applied cryptography and foundations of cryptography with a particular emphasis in blockchain technologies and distributed systems, e-voting and secure multiparty protocols as well as privacy and identity management. His research has been funded by the Horizon 2020 programme (EU), the European Research Council (EU), the Engineering and Physical Sciences Research Council (UK), the Secretariat of Research and Technology (Greece), the National Science Foundation (USA), the Department of Homeland Security (USA), and the National Institute of Standards and Technology (USA). He has received an ERC Starting Grant, a Marie Curie fellowship, an NSF Career Award, and a Fulbright Fellowship. He holds a Ph.D. from the City University of New York and he is a graduate of the Mathematics department of the University of Athens. He has over 100 publications in journals and conference proceedings in the area. He has served as the program chair of the Cryptographers’ Track of the RSA conference in 2011 and the Financial Cryptography and Data Security conference in 2017, as well as the general chair of Eurocrypt 2013. He also served as the program chair of Real World Crypto Symposium 2020 and the Public-Key Cryptography Conference 2020. In 2021 he was elected fellow of the Royal Society of Edinburgh.

Professor (Full) Han-Chieh Chao

(National Dong Hwa University, Taiwan )

Title:

Deep Learning Platform for B5G Mobile Network

Abstract:

The 3G and 4G mobile communications had been developed for many years. The 5G mobile communication is scheduled to be launched in 2020. In the future, a wireless network is of various size of cells and different type of communication technologies, forming a special architecture of Heterogeneous Networks (HetNet). Under the complex network architecture, interference and handover problems are critical challenges in access network. How to efficiently manage small cells and to choose an adequate access mechanism for the better quality of service is a vital research issue. Traditional network architecture can no longer support existing network requirements. It is necessary to develop a novel network architecture. Therefore, this keynote speech will share a solution of deep learning-based B5G mobile network which can enhance and improve communication performance through combing some specific technologies. e.g., deep learning, fog computing, cloud computing, cloud radio access network (C-RAN) and fog radio access network (F-RAN).

Bio:

Han-Chieh Chao received his M.S. and Ph.D. degrees in Electrical Engineering from Purdue University, West Lafayette, Indiana, in 1989 and 1993, respectively. He is currently a professor with the Department of Electrical Engineering, National Dong Hwa University, where he also serves as president. He is also with the Department of Computer Science and Information Engineering, National Ilan University, Taiwan. He was the Director of the Computer Center for Ministry of Education Taiwan from September 2008 to July 2010. His research interests include IPv6, Cross-Layer Design, Cloud Computing, IoT, and 5G Mobile Networks. He has authored or co-authored 4 books and has published about 400 refereed professional research papers. He has completed more than 150 MSEE thesis students and 11 Ph.D. students. He serves as the Editor-in-Chief for the Institution of Engineering and Technology Networks, the Journal of Internet Technology, the International Journal of Internet Protocol Technology, and the International Journal of Ad Hoc and Ubiquitous Computing. He is a Fellow of IET (IEE) and a Chartered Fellow of the British Computer Society. Dr. Chao has been ranked as the top 10 Computer Scientists in Taiwan for 2020 by Guide2Research. Due to Dr. Chao’s contribution of suburban ICT education, he has been awarded the US President's Lifetime Achievement Award and International Albert Schweitzer Foundation Human Contribution Award in 2016, and South East Asia Regional Computer Confederation, SEARCC in 2017.

Professor (Full) Xiangjian He

(University of Nottingham Ningbo China)

Title:

Fault diagnosis model for photovoltaic array using DcCNN

Abstract:

The effective fault diagnosis algorithm for the DC side photovoltaic (PV) array of a PV system (PVS) plays an important role in the operation efficiency and safety for PV power plants. For fault diagnosis models, it may fail to diagnose PV array (PVA) faults without detailed and quite fine fault features, especially line-line faults (LLF) occurring in the PVS that works under complex working conditions like low irradiance conditions and LLF with fault impedance. This talk presents a fault diagnosis scheme to diagnose different PVA faults using a proposed Dual-channel Convolutional Neural Network (DcCNN), which is able to automatically extract features and weight these features for fault classification. The important and fine features from the current and voltage electrical time series graph (ETSG) are extracted respectively by DcCNN in a double input way. Then, a proposed feature selection structure (FSS) is designed to improve the proposed fault diagnosis model capacity for diagnosing PVA faults under various conditions, including LLF, partial shading condition (PSC) and open circuit faults (OCF). Comparing to manually designed features, FSS not only helps DcCNN extract important features from PVA current and voltage automatically but also evaluates extracted features for further classification of DcCNN.

Bio:

Xiangjian (Sean) He is currently a Professor in Computer Science at the University of Nottingham Ningbo China and the Director of Computer Vision and Intelligent Perception Laboratory. He was the Director of Computer Vision and Pattern Recognition Laboratory at the Global Big Data Technologies Centre (GBDTC) at the University of Technology Sydney (UTS) from 2011-2022. He has recently been leading his research teams for deep-learning-based and/or machine-learning-based research on computer vision, data analytics, cybersecurity, etc. He was an IEEE Signal Processing Society Student Committee member. He has played various chair roles in many international conferences such as ACM MM, MMM, ICDAR, IEEE BigDataSE, IEEE BigDataService, IEEE TrustCom, IEEE CIT, IEEE AVSS, IEEE TrustCom, IEEE ICPR and IEEE ICARCV.

Professor (Associate) Stjepan Picek

(Radboud University, Netherlands)

Title:

Deep Learning-based Side-channel Analysis: Lessons Learned and Challenges to be Addressed

Abstract:

In side-channel analysis (SCA), the attacker exploits weaknesses in the physical implementations of cryptographic algorithms. In the last few years, profiling SCA based on deep learning proved very successful in breaking cryptographic implementations even protected with countermeasures. Still, there are many open questions. The first part of this talk will cover the things we know about deep learning-based SCA. More precisely, we will discuss several "success" stories advancing the field. In the second part, we will discuss what we think we know (but want to be sure) about deep learning-based SCA. Finally, in the third part of the talk, we will discuss what we do not know but want to know about deep learning-based SCA.

Bio:

Stjepan Picek is an associate professor at Radboud University, The Netherlands. His research interests are security/cryptography, machine learning, and evolutionary computation. Prior to the associate professor position, Stjepan was an assistant professor at TU Delft, and a postdoctoral researcher at MIT, USA and KU Leuven, Belgium. Stjepan finished his PhD in 2015 with a topic on cryptology and evolutionary computation techniques. Stjepan also has several years of experience working in industry and government. Up to now, Stjepan has given more than 25 invited talks at conferences and summer schools and published more than 130 refereed papers. He was a general co-chair for Eurocrypt 2021, a program committee member and reviewer for a number of conferences and journals, and a member of several professional societies.