The 5th IEEE International Conference on Cyber Security and Cloud Computing
(IEEE CSCloud 2018)
June 22-24, 2018, Shanghai, China.

Keynote Speakers


Sun-Yuan Kung
Princeton University, USA

Bio: Professor S.Y. Kung received his Ph.D. Degree in Electrical Engineering from Stanford University in 1977. In 1974, he was an Associate Engineer of Amdahl Corporation, Sunnyvale, CA. From 1977 to 1987, he was a Professor of Electrical Engineering-Systems of the University of Southern California, L.A. Since 1987, he has been a Professor of Electrical Engineering at the Princeton University. In addition, he held a Visiting Professorship at the Stanford University (1984); and a Visiting Professorship at the Delft University of Technology (1984); a Toshiba Chair Professorship at the Waseda University, Japan (1984); an Honorary Professorship at the Central China University of Science and Technology (1994); and a Distinguished Chair Professorship at the Hong Kong Polytechnic University since 2001. His research interests include VLSI array processors, system modelling and identification, neural networks, wireless communication, sensor array processing, multimedia signal processing, bioinformatic data mining and biometric authentication. Professor Kung has authored more than 400 technical publications and numereous textbooks, Professor Kung has co-authored more than 400 technical publications and numerous textbooks including "VLSI and Modern Signal Processing," with Russian translation, Prentice-Hall (1985), "VLSI Array Processors", with Russian and Chinese translations, Prentice-Hall (1988); "Digital Neural Networks", Prentice-Hall (1993) ; "Principal Component Neural Networks", John-Wiley (1996); and "Biometric Authentication: A Machine Learning Approach", Prentice-Hall (2004). Professor Kung is a Fellow of IEEE since 1988. He served as a Member of the Board of Governors of the IEEE Signal Processing Society (1989-1991). He was a founding member of several Technical Committees (TC) of the IEEE Signal Processing Society , including VLSI Signal Processing TC (1984), Neural Networks for Signal Processing TC (1991) and Multimedia Signal Processing TC (1998), and was appointed as the first Associate Editor in VLSI Area (1984) and later the first Associate Editor in Neural Network (1991) for the IEEE Transactions on Signal Processing. He presently serves on Technical Committees on Multimedia Signal Processing. Since 1990, he has been the Editor-In-Chief of the Journal of VLSI Signal Processing Systems. Professor Kung was a recipient of IEEE Signal Processing Society's Technical Achievement Award for his contributions on "parallel processing and neural network algorithms for signal processing" (1992); a Distinguished Lecturer of IEEE Signal Processing Society (1994) ; a recipient of IEEE Signal Processing Society's Best Paper Award for his publication on principal component neural networks (1996); and a recipient of the IEEE Third Millennium Medal (2000).

Topic: TBD

Time: TBD.

Abstract: TBD




Edwin Sha

Bio: Edwin Hsing-Mean Sha received BS degree from National Taiwan University in 1986, and Ph.D. degree from the Department of Computer Science, Princeton University, USA in 1992. From August 1992 to August 2000, he was with the Department of Computer Science and Engineering at University of Notre Dame, USA. Since 2000, he has been a tenured full professor at the University of Texas at Dallas. From 2012 to 2017, he served as the Dean of College of Computer Science at Chongqing University, China. He is currently a tenured distinguished professor at East China Normal University, Shanghai, China. He has published more than 400 research papers in refereed international conferences and premier journals including over 60 IEEE/ACM Transactions articles. He served as program committee members and chairs of numerous international conferences and editors of many journals. He received many awards including Teaching Award, Microsoft Trustworthy Computing Curriculum Award, NSF CAREER Award, NSFC Overseas Distinguished Young Scholar Award, Chang-Jiang Honorary Chair Professorship, China Thousand-Talent Chair Professorship, etc. He received the ACM TODAES Best Paper Award from ACM Transactions on Design Automation of Electronic Systems, the Editor's pick of the year of 2016 from IEEE Transactions on Computers for his work on SIMFS, and many other best paper awards.

Topic: Towards the Design of Efficient In-Memory Storage Systems

Time: TBD.

Abstract: This talk will present, from the perspective of system software, how to design the most efficient in-memory storage system, including files systems, database systems, etc.. As the emerging technologies of persistent memory, like PCM, MRAM, provide opportunities for preserving data in memory, traditional storage system structures may need re-studying and re-designing. The talk will first present a framework based on a new concept that each file has its own ``Virtual Address Space." A file system called SIMFS is then designed and fully implemented. SIMFS outperforms other in-memory file systems such as Intel's PMFS. We believe that this concept has a great impact to the design of many in-memory storage systems. Based on the concept, we have conducted work on the design and implementation of hybrid file systems, user-level file systems, distributed in-memory file system, etc. This lecture will also present our new efficient and effective index structure, different from B+ trees or alike, for NVM-based relational databases, and present some of our work on NVM-based key-value database. All of the results give the best known ones in literatures.




Professor Geyong Min
University of Exeter, U.K.

Bio: Professor Geyong Min is a Chair in High Performance Computing and Networking and the academic lead of Computer Science in the College of Engineering, Mathematics and Physical Sciences at the University of Exeter, UK. His recent research has been supported by European FP6/FP7, UK EPSRC, Royal Academy of Engineering, Royal Society, and industrial partners including Motorola, IBM, Huawei Technologies, INMARSAT, and InforSense Ltd. Prof. Min is the Co-ordinator of two recently funded FP7 projects: 1) Quality-of-Experience Improvement for Mobile Multimedia across Heterogeneous Wireless Networks; and 2) Cross-Layer Investigation and Integration of Computing and Networking Aspects of Mobile Social Networks. As a key team member and participant, he has made significant contributions to several EU funded research projects on Future Generation Internet. He has published more than 200 research papers in leading international journals including IEEE/ACM Transactions on Networking, IEEE Journal on Selected Areas in Communications, IEEE Transactions on Communications, IEEE Transactions on Wireless Communications, IEEE Transactions on Multimedia, IEEE Transactions on Computers, IEEE Transactions on Parallel and Distributed Systems, and at reputable international conferences, such as SIGCOMM-IMC, ICDCS, IPDPS, GLOBECOM, and ICC. He is an Associated Editor of several international journals, e.g., IEEE Transactions on Computers. He served as the General Chair/Program Chair of a number of international conferences in the area of Information and Communications Technologies.

Topic: Distributed Network Big Data Processing Platform

Time: TBD.

Abstract: With the ever-increasing migration of business services to the Cloud, the past years have witnessed an explosive growth in the volume of network data driven by the popularization of smart mobile devices and pervasive content-rich multimedia applications, creating a critical issue of Internet traffic flooding. How to handle the ever-increasing network traffic has become a pressing challenge. This talk will present a distributed processing platform we have recently developed to support data acquisition from different network domains and achieve effective representation and efficient analysis of heterogeneous network big data. This big data processing platform has the potential to discover valuable insights and knowledge hidden in rich network big data for improving the design, operation, and management of future Internet. The talk offers the theoretical underpinning for efficient analysis of network big data as well as the insights on implementation of distributed data processing platform for online anomaly prediction and detection in future Internet.




Dr. Shui Yu
School of Information Technology,
Deakin University, Australia

Bio: Shui Yu is currently an Associate Professor of School of Information Technology, Deakin University, Australia. Dr Yu’s research interest includes Security and Privacy, Networking, Big Data, and Mathematical Modelling. He has published two monographs and edited two books, more than 200 technical papers, including top journals and top conferences, such as IEEE TPDS, TC, TIFS, TMC, TKDE, TETC, ToN, and INFOCOM. Dr Yu initiated the research field of networking for big data in 2013. His h-index is 29. Dr Yu actively serves his research communities in various roles. He is currently serving the editorial boards of IEEE Communications Surveys and Tutorials, IEEE Communications Magazine, IEEE Internet of Things Journal, IEEE Communications Letters, IEEE Access, and IEEE Transactions on Computational Social Systems. He has served more than 70 international conferences as a member of organizing committee, such as publication chair for IEEE Globecom 2015, IEEE INFOCOM 2016 and 2017, TPC chair for IEEE BigDataService 2015, and ACSW 2017. He is a Senior Member of IEEE, a member of AAAS and ACM, the Vice Chair of Technical Committee on Big Data  of IEEE Communication Society, and a Distinguished Lecturer of IEEE Communication Society.

Topic: Cybersecurity and Privacy: State-of-Art, Challenges, and Opportunities

Time: TBD.

Abstract: Cybersecurity and privacy are two hot topics in our society. However, both of them are mainly uncharted territories, and we have far more questions than answers from applications all the way to theories. In this talk, we review the state-of-art of the fields based on two research topics: distributed denial of service and big data privacy, respectively, aiming to present audience an overview of the current battle ground. We also discuss the problems and challenges that we are facing, and explore the possible promising directions in the fields.






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