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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PickMail: A Serious Game for Email Phishing Awareness Training

Gokul CJ (TCS Research, Tata Consultancy Services Ltd., Pune), Vijayanand Banahatti (TCS Research, Tata Consultancy Services Ltd., Pune), Sachin Lodha (TCS Research, Tata Consultancy Services Ltd., Pune)

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podft: On Accelerating Dynamic Taint Analysis with Precise Path...

Zhiyou Tian (Xidian University), Cong Sun (Xidian University), Dongrui Zeng (Palo Alto Networks), Gang Tan (Pennsylvania State University)

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Progressive Scrutiny: Incremental Detection of UBI bugs in the...

Yizhuo Zhai (University of California, Riverside), Yu Hao (University of California, Riverside), Zheng Zhang (University of California, Riverside), Weiteng Chen (University of California, Riverside), Guoren Li (University of California, Riverside), Zhiyun Qian (University of California, Riverside), Chengyu Song (University of California, Riverside), Manu Sridharan (University of California, Riverside), Srikanth V. Krishnamurthy (University of California, Riverside),…

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ATTEQ-NN: Attention-based QoE-aware Evasive Backdoor Attacks

Xueluan Gong (Wuhan University), Yanjiao Chen (Zhejiang University), Jianshuo Dong (Wuhan University), Qian Wang (Wuhan University)

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