T-VMI: Trusted Virtual Machine Introspection in Cloud Environments

被引:9
|
作者
Jia, Lina [1 ]
Zhu, Min [1 ]
Tu, Bibo [1 ]
机构
[1] Univ Chinese Acad Sci, Chinese Acad Sci, Inst Informat Engn, Beijing, Peoples R China
关键词
Trusted VMI; TrustZone; Semantic Gap;
D O I
10.1109/CCGRID.2017.48
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Nowadays, the vulnerability of cloud environment exposed in security places Virtual Machine Introspection(VMI) at risk: once attackers subvert any layers of cloud environment, such as host, virtual machine manager(VMM) or qemu, VMI will be exposed undoubtedly to those attackers too. Nearly all existing VMI techniques implicitly assume that both VMM by which VMI accesses specific VM data and host which VMI is running on, are nonmalicious and immutable. Unfortunately, this assumption can be potentially violated with the growing shortage of security in cloud environment. Once VMM or host is exploited, attackers can tamper the code or hijack the data of VMI, then, falsify VM information and certifications to Cloud system's administrators who try to make sure the security of specific VM in certain compute node. This paper proposes a new trusted VMI monitor frame: T-VMI, which can avoid the malicious subversion of the routine of VMI. T-VMI guarantees the integrity of VMI code using isolation and the correctness of VMI data using high privilege level instruction and appropriate trap mechanism. This model is evaluated on a simulation environment by using ARM Foundation Model 8.0 and has been presented on a real development ARMv8 JUNO-r0 board. We finished the comprehensive experiments including effectiveness and performance, and the result and analysis show T-VMI has achieved the aim of expected effectiveness with acceptable performance cost.
引用
收藏
页码:478 / 487
页数:10
相关论文
共 50 条
  • [21] CloudPhylactor: Harnessing Mandatory Access Control for Virtual Machine Introspection in Cloud Data Centers
    Taubmann, Benjamin
    Rakotondravony, Noelle
    Reiser, Hans P.
    2016 IEEE TRUSTCOM/BIGDATASE/ISPA, 2016, : 957 - 964
  • [22] A lightweight method for virtual machine introspection
    N. I. Fursova
    P. M. Dovgalyuk
    I. A. Vasil’ev
    V. A. Makarov
    Programming and Computer Software, 2017, 43 : 307 - 313
  • [23] ESI-Cloud: Extending Virtual Machine Introspection for Integrating Multiple Security Services
    Ren, Jiangchun
    Liu, Ling
    Zhang, Da
    Zhou, Huaizhe
    Zhang, Qi
    PROCEEDINGS 2016 IEEE INTERNATIONAL CONFERENCE ON SERVICES COMPUTING (SCC 2016), 2016, : 804 - 807
  • [24] Enforcing Access Controls for the Cryptographic Cloud Service Invocation Based on Virtual Machine Introspection
    Jiang, Fangjie
    Cai, Quanwei
    Guan, Le
    Lin, Jingqiang
    INFORMATION SECURITY (ISC 2018), 2018, 11060 : 213 - 230
  • [25] Virtual machine introspection - Observation or interference?
    Nance, Kara
    Hay, Brian
    Bishop, Matt
    IEEE SECURITY & PRIVACY, 2008, 6 (05) : 32 - 37
  • [26] Virtual Machine Introspection based Spurious Process Detection in Virtualized Cloud Computing Environment
    Kumara, Ajay M. A.
    Jaidhar, C. D.
    2015 1ST INTERNATIONAL CONFERENCE ON FUTURISTIC TRENDS ON COMPUTATIONAL ANALYSIS AND KNOWLEDGE MANAGEMENT (ABLAZE), 2015, : 261 - 267
  • [27] A lightweight method for virtual machine introspection
    Fursova, N. I.
    Dovgalyuk, P. M.
    Vasil'ev, I. A.
    Makarov, V. A.
    PROGRAMMING AND COMPUTER SOFTWARE, 2017, 43 (05) : 307 - 313
  • [28] Policies for Assisted Virtual Machine Selection in Cloud Computing Environments
    Teixeira, Mario Meireles
    Bestavros, Azer
    2015 XXXIII BRAZILIAN SYMPOSIUM ON COMPUTER NETWORKS AND DISTRIBUTED SYSTEMS, 2015, : 228 - 236
  • [29] Towards optimal virtual machine placement methods in cloud environments
    Zuo, Haichun
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2023, 44 (05) : 8663 - 8696
  • [30] Minimizing virtual machine migration probability in cloud computing environments
    Moghaddam, Marjan Jalali
    Esmaeilzadeh, Akram
    Ghavipour, Mina
    Zadeh, Ahmad Khadem
    CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 2020, 23 (04): : 3029 - 3038