MADFAM: MicroArchitectural Data Framework and Methodology

Authors

Tor J. Langehaug [*], Air Force Institute of TechnologyFollow
Scott R. Graham, Air Force Institute of TechnologyFollow
Christine M. Schubert Kabban, Air Force Institute of TechnologyFollow
Brett J. Borghetti, Air Force Institute of TechnologyFollow

Document Type

Article

Publication Date

3-7-2022

Abstract

In the aftermath of Spectre and Meltdown researchers have proposed a variety of attack detection solutions by applying machine learning to data collected from hardware performance monitoring units. Although many microarchitectural attack detection systems provide high-accuracy detection results, the behavior of the underlying data collection mechanisms is not well described or understood. This research introduces the MicroArchitectural Data Framework And Methodology (MADFAM) to prescribe a systematic approach to collecting and preserving the information available in sequences of microarchitectural data. The proposed framework focuses on hardware performance counters (HPCs) as the primary data source. HPC configuration is complex, which makes it difficult for others to reproduce results or advance the state-of-the-art. This framework includes a description of design decisions that HPC research must consider across an array of problem domains, including information security. MADFAM proposes a data collection architecture and evaluation criteria to improve the discussion about the experimental settings and design decisions used in HPC research. The proposed framework evaluation criteria are then used to establish a baseline characterization of time series data that future research can use to compare alternative framework implementations.

Comments

© 2022 The Authors.

This article is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Please fully attribute the citation below in any re-use.

[*] Author Langehaug was an AFIT PhD candidate at the time of publication.

DOI

10.1109/ACCESS.2022.3153313

Source Publication

IEEE Access

Recommended Citation

T. J. Langehaug, S. R. Graham, C. M. Schubert Kabban and B. J. Borghetti, "MADFAM: MicroArchitectural Data Framework and Methodology," in IEEE Access, vol. 10, pp. 23511-23531, 2022, doi: 10.1109/ACCESS.2022.3153313.