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Dokumenttyp:
Konferenzbeitrag 
Art des Konferenzbeitrags:
Textbeitrag / Aufsatz 
Autor(en):
Baehr, Johanna; Bernardini, Alessandro; Sigl, Georg; Schlichtmann, Ulf 
Titel:
Machine Learning and Structural Characteristics for Reverse Engineering 
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: