James Fitts, Chris Fennel (Walmart)

Red Team campaigns simulate real adversaries and provide real value to the organization by exposing vulnerable infrastructure and processes that need to be improved. The challenge is that as organizations scale in size, time between campaign retesting increases. This can lead to gaps in ensuring coverage and finding emerging issues. Automation and simulation of adversarial attacks can be created to address the scale problem. Collecting libraries of Tactics, Techniques and Procedures (TTPs) and testing them via adversarial emulation software. Unfortunately, automation lacks feedback and cannot analyze the data in real time with each test.

To address this problem, we introduce RAMPART (Repeated And Measured Post Access Red Teaming). RAMPART campaigns are very quick campaigns (1 day) meant to bridge the gap between the automation of Red Team simulations and full blown Red Team campaigns. The speed of these campaigns comes from pre-built playbooks backed by Cyber Threat Intelligence (CTI) research. This approach enables a level of freedom to make decisions based on the data the red team analyst sees from their tooling and allows testing further in the attack chain to test detections that could be missed otherwise.

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Mingxuan Liu (Zhongguancun Laboratory; Tsinghua University), Yiming Zhang (Tsinghua University), Xiang Li (Tsinghua University), Chaoyi Lu (Tsinghua University), Baojun Liu (Tsinghua University), Haixin Duan (Tsinghua University; Zhongguancun Laboratory), Xiaofeng Zheng (Institute for Network Sciences and Cyberspace, Tsinghua University; QiAnXin Technology Research Institute & Legendsec Information Technology (Beijing) Inc.)

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SENSE: Enhancing Microarchitectural Awareness for TEEs via Subscription-Based Notification

Fan Sang (Georgia Institute of Technology), Jaehyuk Lee (Georgia Institute of Technology), Xiaokuan Zhang (George Mason University), Meng Xu (University of Waterloo), Scott Constable (Intel), Yuan Xiao (Intel), Michael Steiner (Intel), Mona Vij (Intel), Taesoo Kim (Georgia Institute of Technology)

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Inaudible Adversarial Perturbation: Manipulating the Recognition of User Speech...

Xinfeng Li (Zhejiang University), Chen Yan (Zhejiang University), Xuancun Lu (Zhejiang University), Zihan Zeng (Zhejiang University), Xiaoyu Ji (Zhejiang University), Wenyuan Xu (Zhejiang University)

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Unus pro omnibus: Multi-Client Searchable Encryption via Access Control

Jiafan Wang (Data61, CSIRO), Sherman S. M. Chow (The Chinese University of Hong Kong)

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