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Keynote Lectures

Leveraging Knowledge from Humans and from Data - Why Requirements Engineering (Still) Matters
Eric Yu, University of Toronto, Canada

Integrating Big Data, Data Science and Cyber Security with Applications in Cloud and Internet of Things
Bhavani Thuraisingham, University of Texas at Dallas, United States

 

Leveraging Knowledge from Humans and from Data - Why Requirements Engineering (Still) Matters

Eric Yu
University of Toronto
Canada
 

Brief Bio
Eric Yu is Professor at the Faculty of Information at the University of Toronto. His research interests include conceptual modeling, software requirements engineering, information systems engineering, knowledge management, and enterprise modeling. In his PhD work, he developed the i* framework for social modeling. The work has inspired hundreds of research papers, dozens of PhD theses, and many software tools. A version of i* is part of an international standard. He is co-editor of the MIT Press book series on Information Systems, and is on the editorial boards of the Requirements Engineering journal, IET Software, and the Journal on Data Semantics. He was Program Co- Chair for ER 2008 and 2014, and for CAiSE 2020. He was recipient of the 2019 Peter P. Chen Award.


Abstract
Available soon.



 

 

Integrating Big Data, Data Science and Cyber Security with Applications in Cloud and Internet of Things

Bhavani Thuraisingham
University of Texas at Dallas
United States
 

Brief Bio
Dr. Bhavani Thuraisingham is the Founders Chair Professor of Computer Science and the Executive Director of the Cyber Security Research and Education Institute at the University of Texas at Dallas (UTD). She is also a visiting Senior Research Fellow at Kings College, University of London and an elected Fellow of the ACM, IEEE, the AAAS, the NAI and the BCS. Her research interests are on integrating cyber security and artificial intelligence/data science including as they relate to the cloud, social media and the Internet of Things. She has received several technical and leadership awards including the IEEE CS 1997 Technical Achievement Award, ACM SIGSAC 2010 Outstanding Contributions Award, the IEEE Comsoc Communications and Information Security 2019 Technical Recognition Award, the IEEE CS Services Computing 2017 Research Innovation Award, the ACM CODASPY 2017 Lasting Research Award, the IEEE ISI 2010 Research Leadership Award, and the ACM SACMAT 10 Year Test of Time Awards for 2018 and 2019 (for papers published in 2008 and 2009).  Her 40-year career includes industry (Honeywell), federal research laboratory (MITRE), US government (NSF) and US Academia. Her work has resulted in 130+ journal articles, 300+ conference papers, 180+ keynote and featured addresses, seven US patents, fifteen books, and podcasts. She received her PhD from the University of Wales, Swansea, UK, and the prestigious earned higher doctorate (D. Eng) from the University of Bristol, UK. She has a Certificate in Public Policy Analysis from the London School of Economics and Political Science. 


Abstract

Big Data, Data Science and Security are being integrated to solve many of the security and privacy challenges. For example, machine learning techniques are being applied to solve security problems such as malware analysis and insider threat detection. However, there is also a major concern that the machine learning techniques themselves could be attacked. Therefore, the machine learning techniques are being adapted to handle adversarial attacks. This area is known as adversarial machine learning. In addition, privacy of the individuals is also being violated through these machine learning techniques as it is now possible to gather and analyze vast amounts of data and therefore privacy enhanced data science techniques are being developed.

To assess the developments on the integration of Big Data, Data Science and Security over the past decade and apply them to the Internet of Transportation, the presentation will focus on four aspects. First it will examine the developments on applying Data Science techniques for detecting cyber security problems such as insider threat detection as well as the advances in adversarial machine learning. Some developments on privacy aware and policy-based data management frameworks will also be discussed. Next it will discuss how cloud technologies may be used to securely and privately share the information for various Big Data applications such as the Internet of Things. Finally, it will describe ways in which Big Data, Data Science and Security could be incorporated into these applications. 



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