Improved Test Pattern Generation for Hardware Trojan Detection Using Genetic Algorithm and Boolean Satisfiability
Test generation for Hardware Trojan Horses (HTH) detection is extremely challenging, as Trojans are designed to be triggered by very rare logic conditions at internal nodes of the circuit. In this paper, we propose a Genetic Algorithm (GA) based Automatic Test Pattern Generation (ATPG) technique, en...
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Published in | Cryptographic Hardware and Embedded Systems -- CHES 2015 Vol. 9293; pp. 577 - 596 |
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Main Authors | , , , , |
Format | Book Chapter |
Language | English |
Published |
Germany
Springer Berlin / Heidelberg
2015
Springer Berlin Heidelberg |
Series | Lecture Notes in Computer Science |
Subjects | |
Online Access | Get full text |
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Summary: | Test generation for Hardware Trojan Horses (HTH) detection is extremely challenging, as Trojans are designed to be triggered by very rare logic conditions at internal nodes of the circuit. In this paper, we propose a Genetic Algorithm (GA) based Automatic Test Pattern Generation (ATPG) technique, enhanced by automated solution to an associated Boolean Satisfiability problem. The main insight is that given a specific internal trigger condition, it is not possible to attack an arbitrary node (payload) of the circuit, as the effect of the induced logic malfunction by the HTH might not get propagated to the output. Based on this observation, a fault simulation based framework has been proposed, which enumerates the feasible payload nodes for a specific triggering condition. Subsequently, a compact set of test vectors is selected based on their ability to detect the logic malfunction at the feasible payload nodes, thus increasing their effectiveness. Test vectors generated by the proposed scheme were found to achieve higher detection coverage over large population of HTH in ISCAS benchmark circuits, compared to a previously proposed logic testing based Trojan detection technique. |
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ISBN: | 3662483238 9783662483237 |
ISSN: | 0302-9743 1611-3349 |
DOI: | 10.1007/978-3-662-48324-4_29 |