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Massachusetts Institute of Technology

Hashing hardware : identifying hardware during boot-time system verification

Abstract

dc:description.abstract

Modern systems measure the software loaded at boot-time to ensure the machine starts in a trusted state. Such measurements, however, do not include any information about the underlying hardware of the machine. Recent DRAM-based attacks and the growing complexity of the supply chain attest to the importance of measuring hardware at boot. In this thesis, we propose a technique for designing measurement schemes for hardware components. We then apply this technique to designing and implementing a hardware measurement scheme for DRAM on a real system without hardware modifications. Finally, we evaluate our DRAM hardware measurement scheme and demonstrate that it achieves 89% accuracy in mapping a DRAM measurement to the manufacturing process from which that DRAM was produced.

Degree

thesis:*
Department dc:contributor.department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Grantor dc:publisher
Massachusetts Institute of Technology
Year dc:date.issued
2017

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Chilingirian, Berj Krikor
Advisor dc:contributor.advisor
  • Stelios Sidiroglou-Douskos and Martin Rinard.

Subjects

dc:subject × 1

Rights

dc:rights
Statement dc:rights
  • MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
Language dc:language.iso
eng

Identifiers

dc:identifier.*
Handle dc:identifier.uri
http://hdl.handle.net/1721.1/112837
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/112837

Chain of custody

source
Harvested from
MIT
Base URL
dspace.mit.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Chilingirian, Berj Krikor. Hashing hardware : identifying hardware during boot-time system verification. Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112837