ABSTRACT: Today's software systems are riddled with security vulnerabilities that invite attack. We envisage a secure software design process at the architectural level, in which the security requirements are adequate, thus enabling appropriate security controls to be implemented to mitigate known threats and vulnerabilities. How can we ensure that the security requirements are adequate? In this talk, we tackle this question by focusing on how external online data sources for vulnerabilities, attack patterns, threat intelligence, and other security information can be leveraged, using Natural Language Processing (NLP), to assist designers in validating the adequacy of the security requirements. This validation is done by determining which requirements map to known threats (identified from the external data), which requirements may be extraneous, and which threats may need a closer look to identify new requirements. We will discuss the availability and nature of the external data sources and describe how we employ NLP to process the data to support the design of secure systems. BIOGRAPHY: Dr. Jason Jaskolka is an Assistant Professor in the Department of Systems and Computer Engineering and the Director of the Cyber Security Evaluation and Assurance (CyberSEA) Research Lab at Carleton University, Ottawa, ON, Canada. He received his Ph.D. in Software Engineering in 2015 from McMaster University, Hamilton, ON, Canada. His research interests include cyber security evaluation and assurance, threat modeling, security-by-design, and formal methods and data-driven approaches for software and security engineering. He is interested in applying his research to critical infrastructures, industrial control systems, cyber-physical and distributed systems, and the Internet of Things (IoT). Register

This is the main event that will introduce the Professional Development Series organized by IEEE Ottawa Young Professionals Affinity Group. Speaker: Mohamed K. Emara is a PhD Student in the Department of Electronics at Carleton University, and a Committee Member and Area Chair for IEEE Canada Young Professionals (Region 7). He received the B.Eng. degree in aerospace engineering and the M.A.Sc. degree in electrical and computer engineering from Carleton University in 2016 and 2018, respectively. From 2019-2020 he was the Chair of IEEE Ottawa Young Professionals. He held various other volunteer positions with IEEE Canada and IEEE Ottawa Section. Co-Hosting: This series of talks is co-hosted by IEEE Montreal Young Professionals Affinity Group (YPAG), IEEE Saint-Maurice YPAG, IEEE Canada Eastern Provinces YPAG, IEEE Carleton University and IEEE uOttawa Student Branches, and IEEE Ottawa Women in Engineering. Register

The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, has generated an unprecedented global health crisis, with more than 2.7 million deaths worldwide. Do you want to contribute to the fight against this pandemic? IEEE SIGHT (Special Interest Group on Humanitarian Technology) of the Montreal Section, Vision and Image Processing Research Group of the University of Waterloo, and DarwinAI Corp. invite data scientists, students and professionals working on Artificial Intelligence (AI) to participate in a virtual competition to help medical researchers diagnose COVID-19 with chest X-ray (CXR) images. The ultimate goal is to contribute to the development of highly accurate yet practical AI solutions for detecting COVID-19 cases and, hopefully, accelerating the treatment of those who need it the most. Moreover, this AI for Good initiative will also allow us to take action on at least one of the United Nations Sustainable Development Goals (SDGs), Good Health and Well-Being. The competition is composed of 2 phases: In the First Phase, the challenge consists of designing robust machine learning algorithms to predict if the subjects of study are either COVID-19 positive or COVID-19 negative. The dataset for this competition is the dataset curated by COVID-Net, a global open-source initiative launched by DarwinAI Corp., Canada, and Vision and Image Processing Research Group, University of Waterloo, Canada, for accelerating advancements in machine learning to aid healthcare workers around the world in the fight against the COVID-19 pandemic. More about the COVID-Net initiative and available open-source resources are available here. In the Second Phase, the 10 top teams of the first phase will have the opportunity to refine their solution and submit a proposal for a follow-up project to positively impact society or the academic community. This competition is organized in collaboration with the National Research Council Canada and co-hosted by the IEEE Young Professionals Affinity Groups of Montreal, Ottawa, Toronto and Vancouver Sections, Vancouver Circuit and Systems (CAS) Technical Chapter, the Student Branches of INRS (Institut National de la Recherche Scientifique), the University of Toronto and Vancouver Simon Fraser University,  and WIE (Women In Engineering) Ottawa. It is also largely sponsored by Microsoft, and partially by the IEEE Canada Humanitarian Initiatives Committee and the IEEE Montreal Section. For more details and registration click here!