Detection-as-Code: Build threat detections the way you build software

SHAFT: Secure, Handy, Accurate and Fast Transformer Inference

Andes Y. L. Kei (Chinese University of Hong Kong), Sherman S. M. Chow (Chinese University of Hong Kong)

Passive Inference Attacks on Split Learning via Adversarial Regularization

Xiaochen Zhu (National University of Singapore & Massachusetts Institute of Technology), Xinjian Luo (National University of Singapore & Mohamed bin Zayed University of Artificial Intelligence), Yuncheng Wu (Renmin University of China), Yangfan Jiang (National University of Singapore), Xiaokui Xiao (National University of Singapore), Beng Chin Ooi (National University of Singapore)

Eclipse Attacks on Monero's Peer-to-Peer Network

Ruisheng Shi (Beijing University of Posts and Telecommunications), Zhiyuan Peng (Beijing University of Posts and Telecommunications), Lina Lan (Beijing University of Posts and Telecommunications), Yulian Ge (Beijing University of Posts and Telecommunications), Peng Liu (Penn State University), Qin Wang (CSIRO Data61), Juan Wang (Wuhan University)

Trim My View: An LLM-Based Code Query System for...

Sima Arasteh (University of Southern California), Pegah Jandaghi, Nicolaas Weideman (University of Southern California/Information Sciences Institute), Dennis Perepech, Mukund Raghothaman (University of Southern California), Christophe Hauser (Dartmouth College), Luis Garcia (University of Utah Kahlert School of Computing)

Detection-as-Code: Build threat detections the way you build software

View More Papers

SHAFT: Secure, Handy, Accurate and Fast Transformer Inference

Passive Inference Attacks on Split Learning via Adversarial Regularization

Eclipse Attacks on Monero's Peer-to-Peer Network

Trim My View: An LLM-Based Code Query System for...