On Reverse Engineering-Based Hardware Trojan Detection

Due to design and fabrication outsourcing to foundries, the problem of malicious modifications to integrated circuits (ICs), also known as hardware Trojans (HTs), has attracted attention in academia as well as industry. To reduce the risks associated with Trojans, researchers have proposed different...

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Bibliographic Details
Published in:IEEE transactions on computer-aided design of integrated circuits and systems Vol. 35; no. 1; pp. 49 - 57
Main Authors: Chongxi Bao, Forte, Domenic, Srivastava, Ankur
Format: Journal Article
Language:English
Published: New York IEEE 01.01.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Clustering
Design engineering
Fabrication
Feature extraction
Foundries
Hardware
Hardware Trojan detection
integrated circuit (IC) security and trust
Integrated circuits
K-Means clustering
Layout
Manuals
one-class SVM
Reverse engineering
reverse-engineering based hardware Trojan detection
Risk
Support vector machines
Trojan horses
one-class support vector machine (SVM)
reverse-engineering (RE)-based HT detection
Hardware Trojan (HT) detection
integrated circuit (IC) security and trust
{K} -means clustering
ISSN:0278-0070, 1937-4151
Online Access:Get full text
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Summary:Due to design and fabrication outsourcing to foundries, the problem of malicious modifications to integrated circuits (ICs), also known as hardware Trojans (HTs), has attracted attention in academia as well as industry. To reduce the risks associated with Trojans, researchers have proposed different approaches to detect them. Among these approaches, test-time detection approaches have drawn the greatest attention. Many test-time approaches assume the existence of a Trojan-free (TF) chip/model also known as "golden model." Prior works suggest using reverse engineering (RE) to identify such TF ICs for the golden model. However, they did not state how to do this efficiently. In fact, RE is a very costly process which consumes lots of time and intensive manual effort. It is also very error prone. In this paper, we propose an innovative and robust RE scheme to identify the TF ICs. We reformulate the Trojan-detection problem as clustering problem. We then adapt a widely used machine learning method, {K} -means clustering, to solve our problem. Simulation results using state-of-the-art tools on several publicly available circuits show that the proposed approach can detect HTs with high accuracy rate. A comparison of this approach with our previously proposed approach [1] is also conducted. Both the limitations and application scenarios of the two methods are discussed in detail.
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ISSN:0278-0070
1937-4151
DOI:10.1109/TCAD.2015.2488495