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A Methodology for Enabling Forensic Analysis Using Hypervisor Vulnerabilities Data

Published

Author(s)

Ramaswamy Chandramouli, Anoop Singhal, Duminda Wijesekera, Changwei Liu

Abstract

Hardware/Server Virtualization is a key feature of data centers used for cloud computing services and enterprise computing that enables ubiquitous access to shared system resources. Server virtualization is typically performed by a hypervisor, which provides mechanisms to abstract hardware and system resources from an operating system. Hypervisors are large pieces of software with several thousand lines of code and are therefore known to have vulnerabilities. This document analyzes the recent vulnerabilities associated with two open- source hypervisors - Xen and KVM - as reported by the National Institute of Standards and Technology's (NIST) National Vulnerability Database (NVD), and develops a profile of those vulnerabilities in terms of hypervisor functionality, attack type, and attack source. Based on the predominant number of vulnerabilities in a hypervisor functionality (attack vector), two sample attacks using those attack vectors were launched to exploit those vulnerabilities, and the associated system calls were logged. The objective was to determine the evidence coverage for detecting and reconstructing those attacks and identify techniques required to gather missing evidence.
Citation
NIST Interagency/Internal Report (NISTIR) - 8221
Report Number
8221

Keywords

cloud computing, forensic analysis, hypervisors, KVM, vulnerabilities, Xen

Citation

Chandramouli, R. , Singhal, A. , Wijesekera, D. and Liu, C. (2019), A Methodology for Enabling Forensic Analysis Using Hypervisor Vulnerabilities Data, NIST Interagency/Internal Report (NISTIR), National Institute of Standards and Technology, Gaithersburg, MD, [online], https://doi.org/10.6028/NIST.IR.8221 (Accessed December 12, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created June 4, 2019, Updated June 12, 2019