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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Log4shell: Redefining the Web Attack Surface

Douglas Everson (Clemson University), Long Cheng (Clemson University), and Zhenkai Zhang (Clemson University)

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Semantic-Informed Driver Fuzzing Without Both the Hardware Devices and...

Wenjia Zhao (Xi'an Jiaotong University and University of Minnesota), Kangjie Lu (University of Minnesota), Qiushi Wu (University of Minnesota), Yong Qi (Xi'an Jiaotong University)

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Towards Parallel Binary Code Analysis

Xiaozhu Meng (University of Wisconsin-Madison)

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Fooling the Eyes of Autonomous Vehicles: Robust Physical Adversarial...

Wei Jia (School of Cyber Science and Engineering, Huazhong University of Science and Technology), Zhaojun Lu (School of Cyber Science and Engineering, Huazhong University of Science and Technology), Haichun Zhang (Huazhong University of Science and Technology), Zhenglin Liu (Huazhong University of Science and Technology), Jie Wang (Shenzhen Kaiyuan Internet Security Co., Ltd), Gang Qu (University…

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