PCG：利用预取缓解基于冲突的缓存侧信道攻击
PCG: Mitigating Conflict-based Cache Side-channel Attacks with Prefetching
AI总结：
本文提出PCG，一种基于预取的缓存侧信道防御方案，通过同时增加受害者无关和减少受害者相关的缓存占用变化来扰乱攻击者，在保证安全性的同时实现约1.64%的平均性能提升和仅1.26%的硬件开销。
摘要：
为了防御基于冲突的缓存侧信道攻击，已有研究提出缓存分区或重映射技术，以防止不同安全域之间的组冲突或混淆此类冲突的位置。但这类技术会使缓存设计复杂化，并可能带来显著的性能损失。因此，出现了一些基于预取的轻量级方案，通过引入噪声来干扰攻击者的观察。然而，我们通过实验验证，仅依靠预取引入噪声是不够的，因为攻击者仍然能够可靠地区分受害者的缓存访问。本文提出了一种新颖的基于预取的方案，称为PCG。它结合增加与受害者无关的缓存占用变化和减少与受害者相关的缓存占用变化，通过生成嘈杂且难以区分的缓存访问模式来扰乱攻击者。此外，PCG既可以独立工作，也可以无缝集成到大多数常用的预取器中。我们已在gem5和开源RISC-V核心BOOMv3中实现并评估了PCG。评估结果表明，PCG具有优于现有方案的稳健安全性，同时不会导致显著的性能下降。根据基于SPEC CPU 2017基准测试套件的评估，PCG甚至表现出约1.64%的平均性能提升。此外，它在硬件资源消耗上仅带来1.26%的开销。
英文摘要：
To defend against conflict-based cache side-channel attacks, cache partitioning or remapping techniques were proposed to prevent set conflicts between different security domains or obfuscate the locations of such conflicts. But such techniques complicate cache design and may result in significant performance penalties. Therefore, there have been lightweight prefetching-based schemes proposed to introduce noise to confuse attackers' observation. However, we have validated experimentally that relying on prefetching to only introduce noise is insufficient, as attackers can still reliably distinguish the victim's cache accesses. This paper proposes a novel prefetching-based scheme, called PCG. It combines adding victim-irrelevant cache occupancy changes and reducing victim-relevant cache occupancy changes to disrupt attackers by generating noisy and indistinguishable cache access patterns. Additionally, PCG can either work independently or seamlessly be integrated with most of the commonly used prefetchers. We have implemented and evaluated PCG in both gem5 and the open-source RISC-V core BOOMv3. The evaluation results show the PCG's robust security superior to the existing solutions, while without resulting in significant performance degradation. According to the evaluation based on the SPEC CPU 2017 benchmark suite, PCG even shows an average performance improvement of about 1.64%. Moreover, it incurs only 1.26% overhead on hardware resource consumption.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
