SCAFinder: Formal Verification of Cache Fine-Grained Features for Side Channel Detection

被引:0
作者
Zhang, Shixuan [1 ]
Wang, Haixia [2 ]
Qiu, Pengfei [3 ]
Lyu, Yongqiang [2 ]
Wang, Hongpeng [1 ]
Wang, Dongsheng [4 ,5 ]
机构
[1] Harbin Inst Technol, Sch Comp Sci & Technol, Shenzhen 518055, Guangdong, Peoples R China
[2] Tsinghua Univ, Natl Res Ctr Informat Sci & Technol, Beijing 100084, Peoples R China
[3] Beijing Univ Posts & Telecommun BUPT, Key Lab Trustworthy Distributed Comp & Serv, Minist Educ, Beijing 100867, Peoples R China
[4] Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, Peoples R China
[5] Zhongguancun Lab, Beijing 100094, Peoples R China
基金
中国国家自然科学基金;
关键词
Side-channel attacks; Security; Prefetching; Model checking; Microarchitecture; Coherence; Protocols; Hardware security; formal verification; cache side-channel attacks; model checking; ATTACKS; MODEL;
D O I
10.1109/TIFS.2024.3452002
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Recent research has unveiled numerous cache-timing side-channel attacks exploiting the side effects of fine-grained cache features, such as coherence protocol and prefetch, among others. Traditional modeling methods and verification techniques are insufficient for verifying caches with fine-grained features and detecting cache timing vulnerabilities. There is a necessity for comprehensive verification of such complex cache designs. This paper presents SCAFinder, a verification framework targeting the cache designs with fine-grained features; it identifies cache side-channel attacks through model checking techniques. Specifically, it proposes a modeling methodology for cache designs that enables us to abstract the cache's behavior and latency characteristics. We implement a search algorithm for finding all counterexamples based on open-source model checking software. Subsequently, we add an attack scenario analysis module to discover attacks applicable to specific scenarios. We evaluate SCAFinder on Intel Skylake-X microarchitecture, demonstrating its capability to generate 7 new attack sequences exploiting coherence protocol and prefetch, and 12 new replacement policy-based side channels. As a case study, we successfully built a covert channel for one of the sequences on the real-world processor. To the best of our knowledge, we are the first to implement cross-core replacement policy-based attacks on non-inclusive caches.
引用
收藏
页码:8079 / 8093
页数:15
相关论文
共 47 条
[21]   (Invited Paper) Hardware Trojan Detection by Fine-grained Power Domain Partitioning [J].
Ishikawa, Takahiro ;
Yokooji, Kose ;
Midoh, Yoshihiro ;
Miura, Noriyuki ;
Shintani, Michihiro ;
Shiomi, Jun .
30TH ASIA AND SOUTH PACIFIC DESIGN AUTOMATION CONFERENCE, ASP-DAC 2025, 2025, :1257-1263
[22]   SmartOracle: Generating Smart Contract Oracle via Fine-Grained Invariant Detection [J].
Su, Jianzhong ;
Chen, Jiachi ;
Fang, Zhiyuan ;
Lin, Xingwei ;
Tang, Yutian ;
Zheng, Zibin .
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 2025, 51 (04) :947-959
[23]   Residential power demand side management optimization based on fine-grained mixed frequency data [J].
Wang, Bo ;
Deng, Nana ;
Zhao, Wenhui ;
Wang, Zhaohua .
ANNALS OF OPERATIONS RESEARCH, 2022, 316 (01) :603-622
[24]   Detecting Cache-Based Side Channel Attacks in the Cloud: An Approach with Cascade Detection Mode [J].
Yu, Si ;
Gui, Xiaolin ;
Zhang, Xuejun ;
Lin, Jiancai ;
Dai, Min .
JOURNAL OF INTERNET TECHNOLOGY, 2014, 15 (06) :903-915
[25]   Random CFI (RCFI): Efficient Fine-Grained Control-Flow Integrity Through Random Verification [J].
Park, Moon Chan ;
Lee, Dong Hoon .
IEEE TRANSACTIONS ON COMPUTERS, 2021, 70 (05) :733-745
[26]   Estimating the Fine-Grained PM2.5 for Airbox Sensor Fault Detection in Taiwan [J].
Vivancos, Hector Ordonez ;
Li, Guanyao ;
Peng, Wen-Chih .
2017 CONFERENCE ON TECHNOLOGIES AND APPLICATIONS OF ARTIFICIAL INTELLIGENCE (TAAI), 2017, :54-57
[27]   Winter is here! A decade of cache-based side-channel attacks, detection & mitigation for RSA [J].
Mushtaq, Maria ;
Mukhtar, Muhammad Asim ;
Lapotre, Vianney ;
Bhatti, Muhammad Khurram ;
Gogniat, Guy .
INFORMATION SYSTEMS, 2020, 92
[28]   Fine-Grained Differences-Similarities Enhancement Network for Multimodal Fake News Detection [J].
Wu, Xiaoyu ;
Li, Shi ;
Lai, Zhongyuan ;
Song, Haifeng ;
Hu, Chunfang .
INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2023, 14 (10) :1034-1042
[29]   An adaptive wireless passive human detection via fine-grained physical layer information [J].
Gong, Liangyi ;
Yang, Wu ;
Zhou, Zimu ;
Man, Dapeng ;
Cai, Haibin ;
Zhou, Xiancun ;
Yang, Zheng .
AD HOC NETWORKS, 2016, 38 :38-50
[30]   The Kingsguard OS-level mitigation against cache side-channel attacks using runtime detection [J].
Mushtaq, Maria ;
Yousaf, Muhammad Muneeb ;
Bhatti, Muhammad Khurram ;
Lapotre, Vianney ;
Gogniat, Guy .
ANNALS OF TELECOMMUNICATIONS, 2022, 77 (11-12) :731-747