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Author Griggs, Christopher ♦ Sturton, Cynthia ♦ Chi, Andrew ♦ Stanley, Natalie ♦ Zhang, Rui
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Hardware security ♦ Supervised learning ♦ Processor errata ♦ Security properties
Abstract We present a methodology for identifying security critical properties for use in the dynamic verification of a processor. Such verification has been shown to be an effective way to prevent exploits of vulnerabilities in the processor, given a meaningful set of security properties. We use known processor errata to establish an initial set of security-critical invariants of the processor. We then use machine learning to infer an additional set of invariants that are not tied to any particular, known vulnerability, yet are critical to security. We build a tool chain implementing the approach and evaluate it for the open-source OR1200 RISC processor. We find that our tool can identify 19 (86.4%) of the 22 manually crafted security-critical properties from prior work and generates 3 new security properties not covered in prior work.
Description Affiliation: The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA (Zhang, Rui; Stanley, Natalie; Griggs, Christopher; Chi, Andrew; Sturton, Cynthia)
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 1975-04-01
Publisher Place New York
Journal ACM SIGOPS Operating Systems Review (OPSR)
Volume Number 51
Issue Number 2
Page Count 14
Starting Page 541
Ending Page 554


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Source: ACM Digital Library