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Bridging the Semantic Gap in Virtual Machine Introspection via Online Kernel Data Redirection

Authors:

Zhiqiang LinAuthors Info & Claims

ACM Transactions on Information and System Security (TISSEC), Volume 16, Issue 2

Article No.: 7, Pages 1 - 29

https://doi.org/10.1145/2505124

Published: 01 September 2013 Publication History

Abstract

It is generally believed to be a tedious, time-consuming, and error-prone process to develop a virtual machine introspection (VMI) tool because of the semantic gap. Recent advance shows that the semantic-gap can be largely narrowed by reusing the executed code from a trusted OS kernel. However, the limitation for such an approach is that it only reuses the exercised code through a training process, which suffers the code coverage issues. Thus, in this article, we present Vmst, a new technique that can seamlessly bridge the semantic gap and automatically generate the VMI tools. The key idea is that, through system wide instruction monitoring, Vmst automatically identifies the introspection related data from a secure-VM and online redirects these data accesses to the kernel memory of a product-VM, without any training. Vmst offers a number of new features and capabilities. Particularly, it enables an in-VM inspection program (e.g., ps) to automatically become an out-of-VM introspection program. We have tested Vmst with over 25 commonly used utilities on top of a number of different OS kernels including Linux and Microsoft Windows. The experimental results show that our technique is general (largely OS-independent), and it introduces 9.3X overhead for Linux utilities and 19.6X overhead for Windows utilities on average for the introspected program compared to the native in-VM execution without data redirection.

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Ma BYang YLi JZhang FShen WZhou YMa JMeng WJensen CCremers CKirda E(2023)Travelling the Hypervisor and SSD: A Tag-Based Approach Against Crypto Ransomware with Fine-Grained Data RecoveryProceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security10.1145/3576915.3616665(341-355)Online publication date: 15-Nov-2023
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Paundu AFall DMiyamoto DKadobayashi Y(2018)Leveraging KVM Events to Detect Cache-Based Side Channel Attacks in a Virtualization EnvironmentSecurity and Communication Networks10.1155/2018/42162402018Online publication date: 1-Jan-2018
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Index Terms

Bridging the Semantic Gap in Virtual Machine Introspection via Online Kernel Data Redirection
1. Security and privacy
  1. Intrusion/anomaly detection and malware mitigation
  2. Systems security
    1. Operating systems security
2. Software and its engineering
  1. Software notations and tools
    1. Context specific languages

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Published In

cover image ACM Transactions on Information and System Security

ACM Transactions on Information and System Security Volume 16, Issue 2

September 2013

120 pages

ISSN:1094-9224

EISSN:1557-7406

DOI:10.1145/2516951

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Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 01 September 2013

Accepted: 01 June 2013

Revised: 01 April 2013

Received: 01 December 2012

Published in TISSEC Volume 16, Issue 2

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https://dl.acm.org/doi/10.1145/3576915.3616665
Gnanavel SNarayana KJayashree KNancy PTeressa D(2022)Implementation of Block-Level Double Encryption Based on Machine Learning Techniques for Attack Detection and PreventionWireless Communications & Mobile Computing10.1155/2022/42552202022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/4255220
Paundu AFall DMiyamoto DKadobayashi Y(2018)Leveraging KVM Events to Detect Cache-Based Side Channel Attacks in a Virtualization EnvironmentSecurity and Communication Networks10.1155/2018/42162402018Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1155/2018/4216240
Botacin MGeus Pgrégio A(2018)Who Watches the WatchmenACM Computing Surveys10.1145/319967351:4(1-34)Online publication date: 13-Jul-2018
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Bushouse MReeves DZhao ZAhn GKrishnan RGhinita G(2018)HyperagentsProceedings of the Eighth ACM Conference on Data and Application Security and Privacy10.1145/3176258.3176317(212-223)Online publication date: 13-Mar-2018
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M.A. AC.D. J(2018)Automated multi-level malware detection system based on reconstructed semantic view of executables using machine learning techniques at VMMFuture Generation Computer Systems10.1016/j.future.2017.06.00279:P1(431-446)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1016/j.future.2017.06.002
Bauman EAyoade GLin Z(2015)A Survey on Hypervisor-Based MonitoringACM Computing Surveys10.1145/277511148:1(1-33)Online publication date: 10-Aug-2015
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Aboughadareh SCsallner CAzarmi MPreda MMcDonald J(2014)Mixed-Mode Malware and Its AnalysisProceedings of the 4th Program Protection and Reverse Engineering Workshop10.1145/2689702.2689703(1-12)Online publication date: 9-Dec-2014
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