Sun Hyoung Kim (Penn State), Cong Sun (Xidian University), Dongrui Zeng (Penn State), Gang Tan (Penn State)

Enforcing fine-grained Control-Flow Integrity (CFI) is critical for increasing software security. However, for commercial off-the-shelf (COTS) binaries, constructing high-precision Control-Flow Graphs (CFGs) is challenging, because there is no source-level information, such as symbols and types, to assist in indirect-branch target inference. The lack of source-level information brings extra challenges to inferring targets for indirect calls compared to other kinds of indirect branches. Points-to analysis could be a promising solution for this problem, but there is no practical points-to analysis framework for inferring indirect call targets at the binary level. Value set analysis (VSA) is the state-of-the-art binary-level points-to analysis but does not scale to large programs. It is also highly conservative by design and thus leads to low-precision CFG construction. In this paper, we present a binary-level points-to analysis framework called BPA to construct sound and high-precision CFGs. It is a new way of performing points-to analysis at the binary level with the focus on resolving indirect call targets. BPA employs several major techniques, including assuming a block memory model and a memory access analysis for partitioning memory into blocks, to achieve a better balance between scalability and precision. In evaluation, we demonstrate that BPA achieves a 34.5% precision improvement rate over the current state-of-the-art technique without introducing false negatives.

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V2X Security: Status and Open Challenges

Jonathan Petit (Director Of Engineering at Qualcomm Technologies) Dr. Jonathan Petit is Director of Engineering at Qualcomm Technologies, Inc., where he leads research in security of connected and automated vehicles (CAV). His team works on designing security solutions, but also develops tools for automotive penetration testing and builds prototypes. His recent work on misbehavior protection…

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Practical Blind Membership Inference Attack via Differential Comparisons

Bo Hui (The Johns Hopkins University), Yuchen Yang (The Johns Hopkins University), Haolin Yuan (The Johns Hopkins University), Philippe Burlina (The Johns Hopkins University Applied Physics Laboratory), Neil Zhenqiang Gong (Duke University), Yinzhi Cao (The Johns Hopkins University)

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Demo #6: Impact of Stealthy Attacks on Autonomous Robotic...

Pritam Dash, Mehdi Karimibiuki, and Karthik Pattabiraman (University of British Columbia)

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