Qinhan Tan (Zhejiang University), Zhihua Zeng (Zhejiang University), Kai Bu (Zhejiang University), Kui Ren (Zhejiang University)

Cache conflicts due to deterministic memory-to-cache mapping have long been exploited to leak sensitive information such as secret keys. While randomized mapping is fully investigated for L1 caches, it still remains unresolved about how to secure a much larger last-level cache (LLC). Recent solutions periodically change the mapping strategy to disrupt the crafting of conflicted addresses, which is a critical attack procedure to exploit cache conflicts. Remapping, however, increases both miss rate and access latency. We present PhantomCache for securing an LLC with remapping-free randomized mapping. We propose a localized randomization technique to bound randomized mapping of a memory address within only a limited number of cache sets. The small randomization space offers fast set search over an LLC in a memory access. The intrinsic randomness still suffices to obfuscate conflicts and disrupt efficient exploitation of conflicted addresses. We evaluate PhantomCache against an attacker exploring the state-of-the-art attack with linear-complexity. To secure an 8-bank 16~MB 16-way LLC, PhantomCache confines randomization space of an address within 8 sets and brings only 0.5% performance degradation and 0.5% storage overhead per cache line, which are 3x and 9x more efficient than the state-of-the-art solutions. Moreover, PhantomCache is solely an architectural solution and requires no software change.

View More Papers

SquirRL: Automating Attack Analysis on Blockchain Incentive Mechanisms with...

Charlie Hou (CMU, IC3), Mingxun Zhou (Peking University), Yan Ji (Cornell Tech, IC3), Phil Daian (Cornell Tech, IC3), Florian Tramèr (Stanford University), Giulia Fanti (CMU, IC3), Ari Juels (Cornell Tech, IC3)

Read More

Zoom on the Keystrokes: Exploiting Video Calls for Keystroke...

Mohd Sabra (University of Texas at San Antonio), Anindya Maiti (University of Oklahoma), Murtuza Jadliwala (University of Texas at San Antonio)

Read More

POP and PUSH: Demystifying and Defending against (Mach) Port-oriented...

Min Zheng (Orion Security Lab, Alibaba Group), Xiaolong Bai (Orion Security Lab, Alibaba Group), Yajin Zhou (Zhejiang University), Chao Zhang (Institute for Network Science and Cyberspace, Tsinghua University), Fuping Qu (Orion Security Lab, Alibaba Group)

Read More