Situational Awareness and Adversarial Machine Learning

Ross Anderson
Professor of Security Engineering, Cambridge University, UK

Abstract: As the large complex systems used in applications from autonomous vehicles through network defence to factory automation acquire components that use machine-learning algorithms, situational awareness will become ever more important. Modern machine-learning systems are vulnerable to adversarial inputs, just as humans are vulnerable to deception. Humans, and animals too, manage such risks by being sensitive to the presence of adversaries and taking extra care when an attack is more likely. In recent research, we have been exploring how adversarial samples can be detected more easily than they can be blocked, allowing systems to fall back to more cautious modes of operation. The interaction between machine learning components and service-denial attacks is a fascinating subject that few have studied so far. In short, while classical system resilience may be seen in terms of layered defence and redundancy, that of machine-learning systems may be much more human. Combining the two intelligently could be a new frontier for research.

Ross Anderson

Bio: Ross Anderson is Professor of Security Engineering at Cambridge University. He was one of the founders of the discipline of security economics, and leads the Cambridge Cybercrime Centre, which collects and analyses data about online wickedness. He was also a pioneer of prepayment metering, powerline communications, peer-to-peer systems, hardware tamper-resistance and API security. He is a Fellow of the Royal Society and the Royal Academy of Engineering, as well as a winner of the Lovelace Medal – the UK's top award in computing. He is also the author of the standard textbook "Security Engineering – A Guide to Building Dependable Distributed Systems"

Computation as a dynamical system

Susan Stepney
Professor of Computer Science and Director of the York Cross-disciplinary Centre for Systems Analysis, University of York, UK

Abstract: Computation is often thought of as a branch of discrete mathematics, using the Turing model. That model works well for conventional applications such as word processing, database transactions, and other discrete data processing applications. But much of the world’s computer power resides in embedded devices, sensing and controlling complex physical processes in the real world. Other computational models and paradigms might be better suited to such tasks. I will discuss regarding computation as an open dynamical system, with a particular focus on reservoir computing in non-silicon devices, including our recent work on using magnetic materials as computational substrates. This approach can support smart processing „at the edge“, allow a close integration of sensing and computing in a single conceptual model and physical package, and provides a uniform approach to embodying computation in other dynamical systems.

Susan Stepney

Bio: Susan Stepney is professor of Computer Science at the Department of Computer Science, University of York and Director of the York Cross-disciplinary Centre for Systems Analysis. First class honours degree in Natural Sciences (Theoretical Physics) and a PhD in Astrophysics from the University of Cambridge. Industrial experience at GEC-Marconi, and at Logica UK. Joined the University of York in 2002: research in unconventional computing, complex systems (complex systems modelling and simulation, their emergent properties), artificial life (application of biological principles to engineering domains), natural computing, reservoir computing in materio.

Title: TBD

William D. Casebeer
Director of Artificial Intelligence and Machine Learning in Riverside Research’s Open Innovation Center, USA

William D. Casebeer

Bio: William D. Casebeer, PhD, MA, is Director of Artificial Intelligence and Machine Learning in Riverside Research’s Open Innovation Center. Bill’s lab uses next-generation technology to advance human-machine teaming, neuromorphic computing, object and activity classification and recognition, and defensive and offensive cyberwarfare capabilities. Bill has decades of experience leading interdisciplinary teams of scientists and engineers to creative solutions to pressing national security problems, including Director, Senior Director, and Program Manager roles at Scientific Systems Company, Inc., the Innovation Lab at Beyond Conflict, the Human Systems and Autonomy Lab at Lockheed Martin’s Advanced Technology Laboratories, and at the Defense Advanced Research Projects Agency. Bill retired from active US Air Force duty as a Lieutenant Colonel and intelligence analyst in August 2011 and is a graduate of the Air Force Academy, the University of Arizona, the Naval Postgraduate School, and the University of California at San Diego.


We are accepting submissions! Please submit your paper here.

Nov 19: Dr. William D. Casebeer (Riverside Research’s Open Innovation Center, USA) will present a keynote address.
Nov 16: Deadline Extension: Paper submissions are due Jan 11, 2021.
Nov 16: Prof. Susan Stepney (University at Buffalo, USA) will present a keynote address on Computation as a dynamical system.
Nov 15: Prof. Ross Anderson (University of Cambridge, UK) will present a keynote address on Situational awareness and adversarial machine learning.
Nov 02: Prof. Ann Bisantz (University at Buffalo, USA) and Dr. Emilie Roth (Roth Cognitive Engineering, USA) will instruct a tutorial on Getting Support Right: User and Use System Testing using a Work-centered Approach.
Oct 21: Prof. William Lawless (Paine College, USA) will instruct a tutorial on Interdependence and vulnerability in systems: Applying theory to define situations for autonomous systems. 
Sep 24: The IEEE CogSIMA official website is online! 



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