User: Guest  Login
Document type:
Zeitschriftenaufsatz 
Author(s):
Baehr, Johanna; Bernardini, Alessandro; Sigl, Georg; Schlichtmann, Ulf 
Title:
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 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...    »
 
Keywords:
Netlist reverse engineering, Netlist partitioning, Structural similarity, Malicious design modifications, IP infringement, Logic obfuscation 
Journal title:
Integration 
Year:
2020 
Journal volume:
72 
Pages contribution:
1 - 12 
Print-ISSN:
0167-9260