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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| Vydané v: | IEEE transactions on computer-aided design of integrated circuits and systems Ročník 35; číslo 1; s. 49 - 57 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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New York
IEEE
01.01.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0278-0070, 1937-4151 |
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| Abstract | 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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| AbstractList | 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, [Formula Omitted]-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. 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. |
| Author | Chongxi Bao Forte, Domenic Srivastava, Ankur |
| Author_xml | – sequence: 1 surname: Chongxi Bao fullname: Chongxi Bao email: borisbcx@umd.edu organization: Dept. of Electr. & Comput. Eng., Univ. of Maryland, College Park, MD, USA – sequence: 2 givenname: Domenic surname: Forte fullname: Forte, Domenic email: dforte@ece.ufl.edu organization: Dept. of Electr. & Comput. Eng., Univ. of Florida, Gainesville, FL, USA – sequence: 3 givenname: Ankur surname: Srivastava fullname: Srivastava, Ankur email: ankurs@umd.edu organization: Dept. of Electr. & Comput. Eng., Univ. of Maryland, College Park, MD, USA |
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| Keywords | 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 |
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| SubjectTerms | 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 |
| Title | On Reverse Engineering-Based Hardware Trojan Detection |
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