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 50 条
  • [41] Symbolic Verification of Cache Side-Channel Freedom
    Chattopadhyay, Sudipta
    Roychoudhury, Abhik
    IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS, 2018, 37 (11) : 2812 - 2823
  • [42] AIR Cache: A Variable-Size Block Cache Based on Fine-Grained Management Method
    Li, Yuxiong
    Tan, Yujuan
    Xu, Congcong
    Liu, Duo
    Chen, Xianzhang
    Wang, Chengliang
    Zhou, Mingliang
    Hou, Leong U.
    WEB AND BIG DATA, APWEB-WAIM 2021, PT II, 2021, 12859 : 158 - 177
  • [43] Representation Learning for Fine-Grained Change Detection
    O'Mahony, Niall
    Campbell, Sean
    Krpalkova, Lenka
    Carvalho, Anderson
    Walsh, Joseph
    Riordan, Daniel
    SENSORS, 2021, 21 (13)
  • [44] FgDetector: Fine-grained Android Malware Detection
    Li, Dongfang
    Wang, Zhaoguo
    Li, Lixin
    Wang, Zhihua
    Wang, Yucheng
    Xue, Yibo
    2017 IEEE SECOND INTERNATIONAL CONFERENCE ON DATA SCIENCE IN CYBERSPACE (DSC), 2017, : 311 - 318
  • [45] Fine-grained Evaluation on Face Detection in the Wild
    Yang, Bin
    Yan, Junjie
    Lei, Zhen
    Li, Stan Z.
    2015 11TH IEEE INTERNATIONAL CONFERENCE AND WORKSHOPS ON AUTOMATIC FACE AND GESTURE RECOGNITION (FG), VOL. 1, 2015,
  • [46] Fine-grained Topic Detection and Tracking on Twitter
    Mamo, Nicholas
    Azzopardi, Joel
    Layfield, Colin
    PROCEEDINGS OF THE 13TH INTERNATIONAL JOINT CONFERENCE ON KNOWLEDGE DISCOVERY, KNOWLEDGE ENGINEERING AND KNOWLEDGE MANAGEMENT (KDIR), VOL 1:, 2021, : 79 - 86
  • [47] Fine-Grained Accident Detection: Database and Algorithm
    Yu, Hongyang
    Zhang, Xinfeng
    Wang, Yaowei
    Huang, Qingming
    Yin, Baocai
    IEEE TRANSACTIONS ON IMAGE PROCESSING, 2024, 33 : 1059 - 1069
  • [48] CANCEREMO : A Dataset for Fine-Grained Emotion Detection
    Sosea, Tiberiu
    Caragea, Cornelia
    PROCEEDINGS OF THE 2020 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING (EMNLP), 2020, : 8892 - 8904
  • [49] Fine-grained Conflict Detection of IoT Services
    Chaki, Dipankar
    Bouguettaya, Athman
    2020 IEEE 13TH INTERNATIONAL CONFERENCE ON SERVICES COMPUTING (SCC 2020), 2020, : 321 - 328
  • [50] Channel Interaction Networks for Fine-Grained Image Categorization
    Gao, Yu
    Han, Xintong
    Wang, Xun
    Huang, Weilin
    Scott, Matthew R.
    THIRTY-FOURTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THE THIRTY-SECOND INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE AND THE TENTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2020, 34 : 10818 - 10825