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Titel:

Machine learning and structural characteristics for reverse engineering

Dokumenttyp:
Zeitschriftenaufsatz
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 the impact of errors caused by this extraction and the later partitioning of the netlist on the ability to identify the functionality. Current formal verification based methods which...     »
Stichworte:
Netlist reverse engineering, Netlist partitioning, Structural similarity, Malicious design modifications, IP infringement, Logic obfuscation
Zeitschriftentitel:
Integration
Jahr:
2020
Band / Volume:
72
Seitenangaben Beitrag:
1 - 12
Volltext / DOI:
doi:10.1016/j.vlsi.2019.10.002
WWW:
http://www.sciencedirect.com/science/article/pii/S0167926019303049
Print-ISSN:
0167-9260
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