Cybersecurity - A Game Theory Approach: Issues, modelling and computer science applications

Prof. Dr. Sardar M. N. Islam (Naz)
ISILC, & Decision Sciences and Modelling Program, Victoria University, Australia

Abstract: Cybersecurity is a multiagent system where intelligent agents interact, formulate strategies, fight, cooperate, coordinate, design systems, and plan actions to achieve their goals of cybersecurity or hacking and malicious damages. Game theory analyses and formulates strategies and designs rules or mechanisms for this cybersecurity multiagent system on the basis of artificial intelligence. For specifying, characterising and modelling and designing this intelligent multiagent system, mathematical game theory models of different forms can be developed, such as static, dynamic, evolutionary, differential and stochastic game theory models. Different algorithms such as Nash equilibrium, joint optimisation, evolutionary algorithms, neural networks, genetic algorithms, and other machine learning algorithms can be applied to different game theory models for analysing, solving, and computing these cybersecurity models. Findings from these models are used to formulate strategies, cooperate, coordinate, design systems, and plan actions by different intelligent agents and authorities in cybersecurity. Game theory application in cybersecurity is an important area in computer science for doing highly useful academic and practical cybersecurity activities and for academics and practitioners to build their careers. Therefore, it is necessary to prioritise this area of game theory in cybersecurity in computer science for research and development.

Short Bio: Professor Dr. Sardar M. N. Islam (Naz) is Professior from Victoria University, Australia. As professor he has lived, studied, and worked in different countries and visited (extensively) different regions of the world for a long period, he adopts a global and humanistic approach in his research and academic works and he has undertaken rigorous scientific studies of emerging issues of different disciplines of artificial intelligence, business analytics, digitalisation, management science, etc. His academic work has gained international acclaim, resulting in many (1) Honours and Awards, (1) distinguished visiting or adjunct professorial appointments in different countries, (2) appointment in editorial roles of journals and (3) keynote speeches at international conferences in several countries. He has published 29 scholarly academic books in different disciplines. Each of these books makes significant scientific contributions to the literature. These books are published by prestigious publishers and the majority books are published in highly regarded book series. He has also published about 250 articles, including some top leading international journal articles in his specialised research areas.

Industry 4.0: Transforming the Traditional Industries to Next Generation Smart Factories

Dr. Anand Nayyar
Professor and Scientist, Graduate School, Duy Tan University, Da Nang, Viet Nam

Abstract: Current Industry in almost every aspect is undergoing a transformation towards full digitalization and intelligentization of manufacturing processes via smart connectivity, networked entities, real-time data processing and pervasive information. The fourth Industrial revolution “Industry 4.0” is announced by Germany in 2011 and is characterized by automation and digitization, collaborative robotics, 3D Printing, Optimization and Management of Assets, sharing and security of data, tracking parts from cradle to grave, big data analytics as above all Artificial Intelligence with strong support via Machine Learning and Deep Learning.
In this Lecture, I cover aspects with regard to Industrial Revolution, Driving Forces and Key technologies enabling Industry 4.0, Current challenges faced in real-time implementations, in-depth coverage with regard to Technical Terminologies like Cyber-Physical Systems, Smart Factories, Smart Manufacturing, Intelligent Technical Systems. The lecture will also enlighten current adaption of IIoT (Industrial Internet of Things) cum Automation of various industries across nook and corner around the world. In addition of this, lecture will provide key points towards Research areas in Industry 4.0 and future standard i.e. Industry X.0.

Short Bio: Dr. Anand Nayyar received Ph.D (Computer Science) from Desh Bhagat University in 2017 in the area of Wireless Sensor Networks and Swarm Intelligence. He is currently working in Graduate School, Duy Tan University, Da Nang, Vietnam. A Certified Professional with 75+ Professional certificates from CISCO, Microsoft, Oracle, Google, Beingcert, EXIN, GAQM, Cyberoam and many more. Published 450+ Research Papers in various National & International Conferences, International Journals (Scopus/SCI/SCIE/SSCI Indexed) with High Impact Factor. Member of more than 50+ Associations as Senior and Life Member and also acting as ACM Distinguished Speaker. He has authored/co-authored cum Edited 30+ Books of Computer Science. Associated with more than 500 International Conferences as Programme Committee/Chair/Advisory Board/Review Board member. He has 5 Australian Patents to his credit in the area of Wireless Communications, Artificial Intelligence, IoT and Image Processing. He is currently working in the area of Wireless Sensor Networks, IoT, Swarm Intelligence, Cloud Computing, Artificial Intelligence, Blockchain, Cyber Security, Network Simulation and Wireless Communications. Awarded 30+ Awards for Teaching and Research—Young Scientist, Best Scientist, Young Researcher Award, Outstanding Researcher Award, Excellence in Teaching and many more. He is acting as Associate Editor for Wireless Networks (Springer), IET-Quantum Communications, IET Wireless Sensor Systems, IET Networks, IJDST, IJISP, IJCINI. He is acting as Editor-in-Chief of IGI-Global, USA Journal titled “International Journal of Smart Vehicles and Smart Transportation (IJSVST)”.

Remote sensing and AI

Prof. Huiyu Zhou
School of Informatics, University of Leicester, UK

Abstract of the talk: In this talk, I will first introduce what remote sensing images are about. Then, I describe the characteristics and classification of remote sensing images in different applications. Afterwards, I give an overview of the current challenges in remote sensing, before introducing some of our recent works. Finally, I predict the future work in remote sensing in addition to the summary of the talk as well as the introduction of the open data cube project.

Keywords: Remote sensing; artificial intelligence; challenges; object detection and classification.

Bio of the presenter: Prof. Huiyu Zhou received a Bachelor of Engineering degree in Radio Technology from Huazhong University of Science and Technology of China, and a Master of Science degree in Biomedical Engineering from University of Dundee of United Kingdom, respectively. He was awarded a Doctor of Philosophy degree in Computer Vision from Heriot-Watt University, Edinburgh, United Kingdom. Dr. Zhou currently is a Professor at School of Informatics, University of Leicester, United Kingdom. He has published over 350 peer-reviewed papers in the field. He was the recipient of "CVIU 2012 Most Cited Paper Award", “MIUA 2020 Best Paper Award”, “ICPRAM 2016 Best Paper Award” and was nominated for “ICPRAM 2017 Best Student Paper Award” and "MBEC 2006 Nightingale Prize". Dr. Zhou serves as the Editor-in-Chief of Recent Advances in Electrical & Electronic Engineering and Associate Editor of "IEEE Transaction on Human-Machine Systems", “IEEE Journal of Biomedical and Health Informatics”, “Pattern Recognition”, “PeerJ Computer Science” and “IEEE Access”, and Area Chair of IJCAI and BMVC. He is one of the Technical Committee of “IEEE Cognitive and Development Systems”, “Information Assurance & Intelligent Multimedia-Mobile Communication in IEEE SMC Society”, “Robotics Task Force” and “Biometrics Task Force” of the Intelligent Systems Applications Technical Committee, IEEE Computational Intelligence Society. He has given over 100 invited talks at international conferences, industry and universities, and has served as a chair for 70 international conferences and workshops. His research work has been or is being supported by UK EPSRC, MRC, EU, Royal Society, Leverhulme Trust, Puffin Trust, Alzheimer’s Research UK, Invest NI and industry.

Intelligence Extenics: Five Layer-Intelligence of the Machine Brain

Dr. Wenfeng Wang
School of Electrical and Electronic Engineering, Shanghai Institute of Technology, China

Abstract: In the past decades, machine intelligence has been rapidly developed. The current machine intelligence level can be summarized as “five layer intelligence” - environments sensing, active learning, cognitive computing, intelligent decision making and automatic execution. The final goal is a real intelligence (termed as the machine brain) which would support machines to work as well as the human. Five layer intelligence is not the real intelligence, but it presents a frame of intelligence extenics and it is already on the way to the machine brain.

Short Bio: Dr Wenfeng Wang is currently a full professor of School of Electrical and Electronic Engineering, Shanghai Institute of Technology. He is also the director of International Academy of Visual Art and Engineering in London and Shanghai JWE Technological Research Center. He has been invited as a tenure professor and the editor in chief of International Journal of Electronics and Engineering (IJEEE). He is also a key tallent in Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences (2018- ). A reviewer of many SCI journals, including some top journals - Water Research, Science China-Information Sciences, Science of the Total Environment, Environmental Pollution, IEEE Transactions on Automation Science and Engineering and etc. A keynote speaker of AMICR2019, IACICE2020, OAES2020, AICS2021, 3DIT-MSP&DL2020 and etc. A scientist in chief of RealMax, Shanghai Lingang Artificial Intelligence Laboratory and etc.

Gray Box: Deep Reinforcement Learning-enabled Approach for Large-Scale Dynamic System Simulation

Dr. Yang Yang, IEEE Fellow
ShanghaiTech University, China

Abstract(CN)： The massive data generated by large-scale dynamic systems makes its optimization facing a big challenge. Traditional White Box-based methods directly model the internal operating mechanism of the system, so massive amounts of measured data need to be handled, which is costly and time-consuming. The poor interpretability of the Black Box-based methods makes it difficult to adapt to the dynamic environments. In this talk, we propose a novel Gray Box-based approach namely Deep Reinforcement Learning-enabled Constraint Set Inversion Algorithm (DRESIA), which establishes a quantitative model of the nonlinear interoperability effects of system internal states which simplifies the White Box's complex mechanism of reconstruction and prediction and retains the interpretability of the model, therefore improves the prediction efficiency of feasible region while also improving the generalization ability.

Personal Info(CN)： Dr. Yang Yang is a full professor at ShanghaiTech University, China, serving as the Master of Kedao College and the Director of Shanghai Institute of Fog Computing Technology (SHIFT). He is also an adjunct professor with the Research Center for Network Communication at Peng Cheng Laboratory, China. Yang's research interests include fog computing networks, service-oriented collaborative intelligence, wireless sensor networks, IoT applications, and advanced testbeds and experiments. He has published more than 300 papers and filed more than 80 technical patents in these research areas. Yang is a Fellow of the IEEE.

Robots and Deep Bayesian Learning: Robots capable of self-healing and adapting

Dr. Andre Rosendo
ShanghaiTech University, China

Abstract(EN)：Abstract: Although current artificial intelligence (AI) explores learning techniques to create thinking machines through simulations, I argue that the interaction with the real-world is essential for the convergence in true intelligence. With this in mind, I present the Deep Bayesian Learning framework applied to robots, either by a robot capable of creating other robots and improving their performance over time, or, more recently, by our latest experiments incrementally cutting one of the legs of a robot and using Bayesian Optimization to adapt the gait of that robot. I will talk about robots capable of combining Deep Learning and Bayesian Learning to simultaneously alter their morphology and control. The future of robotics remains uncertain, but the capacity of adapting their design and altering their morphology to fulfil tasks more effectively can be a driving force for future Robotics.

Bio: Dr. Andre Rosendo received his Master and Ph.D. degrees from Hokkaido University and Osaka university, in 2011 and 2014, respectively, and finished his postdoc at the University of Cambridge in 2017. He started as a Tenure Track Assistant Professor at ShanghaiTech University at the same year.
He has published more than 50 scientific papers in more than 15 IEEE conferences, worked as Program Committee for a few IEEE conferences and is currently the Associate Editor of the journals Frontiers in Robotics and AI and the Journal of Robotic and Intelligent Systems. In 2019 and 2020 he was awarded the 国家自然科学基金外国学者研究基金 Grant, the 上海高校青年东方学者岗Grant and the 年上海市外国专家项目Award.

Speeding Up IPv4 Connections via IPv6 Infrastructure

Prof. Dr. Xin Wang
School of Computer Science, Fudan University, Shanghai, China

Abstract: In the transition process from IPv4 to IPv6, the lack of customer demand remains a major problem for Internet Service Providers. With the increasing traffic in IPv4 networks, the ISPs' operational cost is growing while the user experience will be degraded. We propose a solution for these problems by transferring IPv4 traffic through the IPv6 core network. By providing better services for IPv4 end users, such as stabler connections, lower latency and better QoS, our solution can serve as an incentive for ISPs to gradually upgrade to pure IPv6 networks. In this demo, we showcase that better service quality for IPv4 end-to-end connections can be acquired by transferring traffic from heavy-loaded IPv4 core network to light-loaded IPv6 core network, using stateless IPv4/IPv6 translation techniques.

Short Bio: Xin Wang is a professor at Fudan University, Shanghai, China. He received his BS Degree in Information Theory and MS Degree in Communication and Electronic Systems from Xidian University, China, in 1994 and 1997, respectively. He received his Ph.D. Degree in Computer
Science from Shizuoka University, Japan, in 2002. His research interests include quality of network service, next-generation network architecture, mobile Internet and network coding.