Kerem Arikan (Binghamton University), Abraham Farrell (Binghamton University), Williams Zhang Cen (Binghamton University), Jack McMahon (Binghamton University), Barry Williams (Binghamton University), Yu David Liu (Binghamton University), Nael Abu-Ghazaleh (University of California, Riverside), Dmitry Ponomarev (Binghamton University)

Protection of cache hierarchies from side-channel attacks is critical for building secure systems, particularly the ones using Trusted Execution Environments (TEEs). In this paper, we consider the problem of efficient and secure fine-grained partitioning of cache hierarchies and propose a framework, called Secure Hierarchies for TEEs (TEE-SHirT). In the context of a three-level cache system, TEE-SHirT consists of partitioned shared last-level cache, partitioned private L2 caches, and non-partitioned L1 caches that are flushed on context switches and system calls. Efficient and correct partitioning requires careful design. Towards this goal, TEE-SHirT makes three contributions: 1) we demonstrate how the hardware structures used for holding cache partitioning metadata can be effectively virtualized to avoid flushing of cache partition content on context switches and system calls; 2) we show how to support multi-threaded enclaves in TEE-SHirT, addressing the issues of coherency and consistency that arise with both intra-core and inter-core data sharing; 3) we develop a formal security model for TEE-SHirT to rigorously reason about the security of our design.

View More Papers

CAN-MIRGU: A Comprehensive CAN Bus Attack Dataset from Moving...

Sampath Rajapaksha, Harsha Kalutarage (Robert Gordon University, UK), Garikayi Madzudzo (Horiba Mira Ltd, UK), Andrei Petrovski (Robert Gordon University, UK), M.Omar Al-Kadri (University of Doha for Science and Technology)

Read More

WIP: Security Vulnerabilities and Attack Scenarios in Smart Home...

Haoqiang Wang (Chinese Academy of Sciences, University of Chinese Academy of Sciences, Indiana University Bloomington), Yichen Liu (Indiana University Bloomington), Yiwei Fang, Ze Jin, Qixu Liu (Chinese Academy of Sciences, University of Chinese Academy of Sciences, Indiana University Bloomington), Luyi Xing (Indiana University Bloomington)

Read More

GNNIC: Finding Long-Lost Sibling Functions with Abstract Similarity

Qiushi Wu (University of Minnesota), Zhongshu Gu (IBM Research), Hani Jamjoom (IBM Research), Kangjie Lu (University of Minnesota)

Read More