Keynote 1: Dr. Ravi Sandhu, ACM Fellow, IEEE Fellow, and AAAS Fellow, Professor at University of Texas at San Antonio, USA

Title: Access Control Convergence: Challenges and Opportunities
(Keynote Speech Video)

Abstract: There have been a handful of ground-breaking concepts in access control over the past half century which have received significant traction in practical deployments. These include the fundamental policy-mechanism and operational-administrative distinctions, along with the authorization models of discretionary access control (DAC), mandatory access control (MAC), role-based access control (RBAC), attribute-based access control (ABAC) and relationship-based access control (ReBAC). In this talk we will argue that modern cyber systems require an effective convergence of these concepts, in that they must coexist in mutually supportive synergy. We will highlight some challenges and opportunities in making this vision a practical reality.

Short-Bio: Ravi Sandhu is Professor of Computer Science, Executive Director of the Institute for Cyber Security and Lead PI of the NSF Center for Security and Privacy Enhanced Cloud Computing at the University of Texas at San Antonio, where he holds the Lutcher Brown Endowed Chair in Cyber Security. Previously he served on the faculty at George Mason University (1989-2007) and Ohio State University (1982-1989). He holds BTech and MTech degrees from IIT Bombay and Delhi, and MS and PhD degrees from Rutgers University. He is a Fellow of IEEE, ACM, AAAS and the National Academy of Inventors. He has received numerous awards from IEEE, ACM, NSA, NIST and IFIP, including the 2018 IEEE Innovation in Societal Infrastructure award for seminal work on role-based access control (RBAC). A prolific and highly cited author, his research has been funded by NSF, NSA, NIST, DARPA, AFOSR, ONR, AFRL, ARO and private industry. His seminal papers on role-based access control established it as the dominant form of access control in practical systems. His numerous other models and mechanisms have also had considerable real-world impact. He served as Editor-in-Chief of the IEEE Transactions on Dependable and Secure Computing, and previously as founding Editor-in-Chief of ACM Transactions on Information and System Security. He was Chairman of ACM SIGSAC, and founded the ACM Conference on Computer and Communications Security, the ACM Symposium on Access Control Models and Technologies and the ACM Conference on Data and Application Security and Privacy. He has served as General Chair, Steering Committee Chair, Program Chair and Committee Member for numerous security conferences. He has consulted for leading industry and government organizations, and has lectured all over the world. He is an inventor on 31 security technology patents and has accumulated over 45,000 Google Scholar citations for his papers. At UTSA his team seeks to pursue world-leading research in both the scientific foundations of cyber security and their applications in diverse 21st century cyber technology domains, including cloud computing, internet of things, autonomous vehicles, big data and blockchain. Particular focus is on foundations and technology of attribute-based access control (ABAC) as a successor to RBAC in these contexts, and on converegnce of access control concepts to solve real-world challenges. His web site is at www.profsandhu.com.

Keynote 2: Dr. Vincenzo Piuri, IEEE Fellow and ACM Distinguished Scientist, Professor at The University of Milan, Italy

Title: Artificial Intelligence for Advanced Biometrics
(Keynote Speech Video)

Abstract: Biometrics concerns the study of automated methods for identifying an individual by measuring one or more physical or behavioral features of him. Certain physical human features or behaviors are characteristics that are specific and can be uniquely associated to one person. Retinas, iris, DNA, fingerprint, palm print, or pattern of finger lengths are typical physical features that are specific to individuals. Also the voice print, gait, or handwriting can be used to this purpose. Nowadays biometrics is rapidly evolving. This science is getting more and more accurate in identifying persons and behaviors. Consequently, these technologies become more and more attractive and effective in critical applications, such as to create safe personal IDs, to control the access to personal information or physical areas, to recognize terrorists or criminals, to study the movements of people, and to monitor the human behavior.
The use of biometrics in the real life often requires very complex signal and image processing and scene analysis, for example encompassing biometric feature extraction and identification, individual tracking, face tracking, eye tracking, liveness/anti-spoofing tests, and facial expression recognition. Artificial intelligence techniques (including neural networks, fuzzy logic, evolutionary computing, and multi-agent systems) have been proved to be useful and effective in addressing this kind of data processing, especially when it is difficult to identify an algorithm while sufficiently descriptive examples are available, or when fuzzy descriptions are more natural to capture the essence of the problem, or when complex non-linear optimization is needed, or when multiple agents cooperate in solving the application problem.
This talk will review the domain of biometrics, its applications in various domains and the relevance of artificial intelligence, in particular neural networks and deep learning to effectively solve various problems in these applications.

Short-Bio: Vincenzo Piuri has received his Ph.D. in computer engineering at Polytechnic of Milan, Italy (1989). He is Full Professor in computer engineering at the University of Milan, Italy (since 2000). He has been Associate Professor at Polytechnic of Milan, Italy and Visiting Professor at the University of Texas at Austin, USA, and visiting researcher at George Mason University, USA.
His main research interests are: artificial intelligence, computational intelligence, intelligent systems, machine learning, pattern analysis and recognition, signal and image processing, biometrics, intelligent measurement systems, industrial applications, digital processing architectures, fault tolerance, cloud computing infrastructures, and internet-of-things. Original results have been published in 400+ papers in international journals, proceedings of international conferences, books, and book chapters.
He is Fellow of the IEEE, Distinguished Scientist of ACM, and Senior Member of INNS. He is President of the IEEE Systems Council (2020-21) and IEEE Region 8 Director-elect (2021-22), and has been IEEE Vice President for Technical Activities (2015), IEEE Director, President of the IEEE Computational Intelligence Society, Vice President for Education of the IEEE Biometrics Council, Vice President for Publications of the IEEE Instrumentation and Measurement Society and the IEEE Systems Council, and Vice President for Membership of the IEEE Computational Intelligence Society. He has been Editor-in-Chief of the IEEE Systems Journal (2013-19). He is Associate Editor of the IEEE Transactions on Cloud Computing and has been Associate Editor of the IEEE Transactions on Computers, the IEEE Transactions on Neural Networks, the IEEE Transactions on Instrumentation and Measurement, and IEEE Access.
He received the IEEE Instrumentation and Measurement Society Technical Award (2002) and the IEEE TAB Hall of Honor (2019). He is Honorary Professor at: Obuda University, Hungary; Guangdong University of Petrochemical Technology, China; Northeastern University, China; Muroran Institute of Technology, Japan; Amity University, India; and Galgotias University, India.

Keynote 3: Dr. Yang Xiao, IEEE Fellow and IET Fellow, Professor at The University of Alabama, United States

Title: Electricity Theft Detection via Modeling Attackers’ Behaviors

Abstract: Smart meters may potentially be attacked or compromised to cause certain security risks including losing tons of money each year due to thefts. It is challenging to identify malicious meters when there are a large number of users. In this talk, three detection methods are introduced: approximation-based approaches including NFD for electricity theft detection, FNFD for fast electricity theft detection and verification, and CNFD for colluded electricity theft detection. In our methods, we model attackers’ behaviors mathematically and understand attackers thoroughly so that we can detect attackers better.

Short-Bio: Dr. Yang Xiao is currently a Full Professor with the Department of Computer Science, The University of Alabama, Tuscaloosa, AL, USA. His current research interests include cyber-physical systems, the Internet of Things, security, wireless networks, smart grid, and telemedicine. He has published over 300 SCI-indexed journal papers (including over 50 IEEE/ACM transactions papers) and 250 EI indexed refereed conference papers related to these research areas. He was a Voting Member of the IEEE 802.11 Working Group from2001 to 2004, involving the IEEE 802.11 (WIFI) standardization work. He is IEEE Fellow and an IET Fellow. He currently serves as the Editor-in-Chief of Cyber-Physical Systems (Journal). He has served an Editorial Board or Associate Editor of 20 international journals, including the IEEE Transactions on Cybernetics since 2020, IEEE Transactions on Systems, Man, and Cybernetics: Systems (2014-2015), IEEE Transactions on Vehicular Technology (2007-2009), and IEEE Communications Survey and Tutorials (2007-2014). He has served as a Guest Editor over 20 times of different international journals, including the IEEE Transactions on Network Science and Engineering, IEEE Network, IEEE Wireless Communications, and ACM/Springer Mobile Networks and Applications (MONET).

Keynote 4: Dr. My T. Thai, IEEE Fellow, Professor at the University of Florida, USA

Title: Provable Guarantees on Privacy in the Age of Adversarial Learning

Abstract: Despite the impressive feats of using deep learning models in many application domains, researchers and the public have grown alarmed by two unsettling deficiencies of these otherwise powerful models: 1) they are prone to interference or deception from adversarial attacks, and 2) they can be exploited to reveal sensitive information of private training data. Simultaneously guaranteeing both user privacy and robustness against adversarial attacks is of utmost need, unfortunately, very challenging. In this talk, we will discuss a core foundation of privacy preserving in adversarial learning, to better address the trade-offs between privacy loss, certified defenses, and model performance.

Short-Bio: My T. Thai is a University of Florida (UF) Professor of Computer & Information Science & Engineering and Associate Director of UF Nelms Institute for the Connected World. Dr. Thai's current research interests include explainable AI, AI Security and Privacy, and Optimization. The results of her work have led to 7 books and 250+ publications in highly ranked international journals and conferences, including several best paper awards from the IEEE and ACM. Dr. Thai received many recognitions, including UF Research Foundation professorship, IoT Term Endowed professorship, NSF CAREER Award, and DTRA Young Investigator Award. She is an IEEE Fellow. Among many professional activities, Dr. Thai currently serves as Editor-in-Chief of the Journal of Combinatorial Optimization, and EiC of the IET Blockchain journal.

Keynote 5: Dr. Wanlei Zhou, Vice Rector (AcademicAffairs), Dean of Institute of Data Science, Professor at City University of Macau, Macau

Title: Dealing with Malicious Agents in Intelligent Multi-Agent Applications
(Keynote Speech Video)

Abstract: Multi-agent systems, especially intelligent multi-agent systems, are widely used in many applications including auto-driving, disaster response, drone swarms, robotics, online trading, IoT, social structure modelling and surveillance etc. However, wide applications of multi-agent systems also bring serious security and privacy issues. For example, disasters will happen if an autonomous car is controlled by a malicious user; and confusions will arise if adversaries use deep-fake to create fake news items. It is almost impossible to predict and control the behaviours of malicious agents (adversaries and malicious users). One way to address the security and privacy issues caused by these malicious agents is to make sure that the applications still function correctly despite the presence of them. In this presentation we focus on multi-agent security and privacy, aiming to overcome the security and privacy issues in two situations: the presence of malicious agents giving false advices and the privacy-preserving in multi-agent planning. In both cases, we aim to avoid and reduce the impact of malicious agents to the applications instead of identifying and eliminating them, and we adopt the differential privacy technique to achieve our goal. The case studies are based on our recent work shown below.

1. Dayong Ye, Tianqing Zhu, Wanlei Zhou, and Philip S. Yu, "Differentially Private Malicious Agent Avoidance in Multiagent Advising Learning", IEEE Transactions on Cybernetics, 50(10): 4214-4227 (2020).
2. Dayong Ye, Tianqing Zhu, Zishuo Cheng, Wanlei Zhou and Philip S. Yu, "Differential Advising in Multiagent Reinforcement Learning", accepted by IEEE Transactions on Cybernetics, early access:
https://ieeexplore.ieee.org/document/9269516
3. Dayong Ye; Tianqing Zhu; Sheng Shen; Wanlei Zhou; Philip Yu. "Differentially Private Multi-Agent Planning for Logistic-like Problems", accepted by IEEE Transactions on Dependable and Secure Computing. doi: 10.1109/TDSC.2020.3017497. Available online:
https://ieeexplore.ieee.org/abstract/document/9170873.

Short-Bio: Professor Wanlei Zhou is currently the Vice Rector (Academic Affairs) and Dean of Institute of Data Science, City University of Macau, Macao SAR, China. He received the B.Eng and M.Eng degrees from Harbin Institute of Technology, Harbin, China in 1982 and 1984, respectively, and the PhD degree from The Australian National University, Canberra, Australia, in 1991, all in Computer Science and Engineering. He also received a DSc degree (a higher Doctorate degree) from Deakin University in 2002. Before joining City University of Macau, Professor Zhou held various positions including the Head of School of Computer Science in University of Technology Sydney, Australia, the Alfred Deakin Professor, Chair of Information Technology, Associate Dean, and Head of School of Information Technology in Deakin University, Australia. Professor Zhou also served as a lecturer in University of Electronic Science and Technology of China, a system programmer in HP at Massachusetts, USA; a lecturer in Monash University, Melbourne, Australia; and a lecturer in National University of Singapore, Singapore. His main research interests include security, privacy, and distributed computing. Professor Zhou has published more than 400 papers in refereed international journals and refereed international conferences proceedings, including many articles in IEEE transactions and journals.