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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Research on the Reliability and Fairness of Opinion Retrieval...

Zhuo Chen, Jiawei Liu, Haotan Liu (Wuhan University)

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DeGPT: Optimizing Decompiler Output with LLM

Peiwei Hu (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China), Ruigang Liang (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China), Kai Chen (Institute of Information Engineering, Chinese Academy of Sciences, China)

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Architecting Trigger-Action Platforms for Security, Performance and Functionality

Deepak Sirone Jegan (University of Wisconsin-Madison), Michael Swift (University of Wisconsin-Madison), Earlence Fernandes (University of California San Diego)

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Separation is Good: A Faster Order-Fairness Byzantine Consensus

Ke Mu (Southern University of Science and Technology, China), Bo Yin (Changsha University of Science and Technology, China), Alia Asheralieva (Loughborough University, UK), Xuetao Wei (Southern University of Science and Technology, China & Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, SUSTech, China)

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