Machine learning and structural characteristics for reverse engineering

Johanna Baehr, Georg Sigl, Alessandro Bernardini, Ulf Schlichtmann

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Scopus citations

Abstract

In the past years, much of the research into hardware reverse engineering has focused on the abstraction of gate level netlists to a human readable form. However, none of the proposed methods consider a realistic reverse engineering scenario, where the netlist is physically extracted from a chip. This paper analyzes how errors caused by this extraction and the later partitioning of the netlist affect the ability to identify the functionality. Current formal verification based methods, which compare against a golden model, are incapable of dealing with such erroneous netlists. Two new methods are proposed, which focus on the idea that structural similarity implies functional similarity. The first approach uses fuzzy structural similarity matching to compare the structural characteristics of an unknown design against designs in a golden model library using machine learning. The second approach proposes a method for inexact graph matching using fuzzy graph isomorphisms, based on the functionalities of gates used within the design. For realistic error percentages, both approaches are able to match more than 90% of designs correctly. This is an important first step for hardware reverse engineering methods beyond formal verification based equivalence matching.

Original languageEnglish
Title of host publicationASP-DAC 2019 - 24th Asia and South Pacific Design Automation Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages96-103
Number of pages8
ISBN (Electronic)9781450360074
DOIs
StatePublished - 21 Jan 2019
Event24th Asia and South Pacific Design Automation Conference, ASPDAC 2019 - Tokyo, Japan
Duration: 21 Jan 201924 Jan 2019

Publication series

NameProceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC

Conference

Conference24th Asia and South Pacific Design Automation Conference, ASPDAC 2019
Country/TerritoryJapan
CityTokyo
Period21/01/1924/01/19

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