Jim Alves-Foss, Varsha Venugopal (University of Idaho)

The effectiveness of binary analysis tools and techniques is often measured with respect to how well they map to a ground truth. We have found that not all ground truths are created equal. This paper challenges the binary analysis community to take a long look at the concept of ground truth, to ensure that we are in agreement with definition(s) of ground truth, so that we can be confident in the evaluation of tools and techniques. This becomes even more important as we move to trained machine learning models, which are only as useful as the validity of the ground truth in the training.

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Demo #4: Recovering Autonomous Robotic Vehicles from Physical Attacks

Pritam Dash (University of British Columbia) and Karthik Pattabiraman (University of British Columbia)

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Understanding MPU Usage in Microcontroller-based Systems in the Wild

Wei Zhou, Zhouqi Jiang (School of Cyber Science and Engineering, Huazhong University of Science and Technology), Le Guan (School of Computing, University of Georgia)

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The hard things about analyzing 1’s and 0’s...

Dr. David Brumley, Carnegie Mellon University - ForAllSecure

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Local and Central Differential Privacy for Robustness and Privacy...

Mohammad Naseri (University College London), Jamie Hayes (DeepMind), Emiliano De Cristofaro (University College London & Alan Turing Institute)

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