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Titel:

Machine Learning and Structural Characteristics for Reverse Engineering

Dokumenttyp:
Konferenzbeitrag
Art des Konferenzbeitrags:
Textbeitrag / Aufsatz
Autor(en):
Baehr, Johanna; Bernardini, Alessandro; Sigl, Georg; Schlichtmann, Ulf
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 comp...     »
Stichworte:
Machine Learning, Reverse Engineering
Dewey-Dezimalklassifikation:
620 Ingenieurwissenschaften
Kongress- / Buchtitel:
24th Asia and South Pacific Design Automation Conference Conference (ASPDAC’19)
Kongress / Zusatzinformationen:
Tokyo, Japan
Datum der Konferenz:
21.01.-24.01.2019
Jahr:
2019
Quartal:
1. Quartal
Jahr / Monat:
2019-01
Monat:
Jan
Reviewed:
ja
Sprache:
en
Volltext / DOI:
doi:10.1145/3287624.3288740
WWW:
http://www.aspdac.com/aspdac2019/
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