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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BGP-iSec: Improved Security of Internet Routing Against Post-ROV Attacks

Cameron Morris (University of Connecticut), Amir Herzberg (University of Connecticut), Bing Wang (University of Connecticut), Samuel Secondo (University of Connecticut)

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Security-Performance Tradeoff in DAG-based Proof-of-Work Blockchain Protocols

Shichen Wu (1. School of Cyber Science and Technology, Shandong University 2. Key Laboratory of Cryptologic Technology and Information Security, Ministry of Education), Puwen Wei (1. School of Cyber Science and Technology, Shandong University 2. Quancheng Laboratory 3. Key Laboratory of Cryptologic Technology and Information Security, Ministry of Education), Ren Zhang (Cryptape Co. Ltd. and…

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Towards Real-time Voice Interaction Data Collection Monitoring and Ambient...

Tu Le (University of California, Irvine), Zixin Wang (Zhejiang University), Danny Yuxing Huang (New York University), Yaxing Yao (Virginia Tech), Yuan Tian (University of California, Los Angeles)

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