TY - JOUR
T1 - Exposing Hardware Trojans in Embedded Platforms via Short-Term Aging
AU - Surabhi, Virinchi Roy
AU - Krishnamurthy, Prashanth
AU - Amrouch, Hussam
AU - Henkel, Jorg
AU - Karri, Ramesh
AU - Khorrami, Farshad
N1 - Funding Information:
Manuscript received April 18, 2020; revised June 12, 2020; accepted July 6, 2020. Date of publication October 2, 2020; date of current version October 27, 2020. This work was supported in part by the Office of Naval Research under Grant N00014-18-1-2672. This article was presented in the International Conference on Compilers, Architecture, and Synthesis for Embedded Systems 2020 and appears as part of the ESWEEK-TCAD special issue. (Corresponding author: Virinchi Roy Surabhi.) Virinchi Roy Surabhi, Prashanth Krishnamurthy, Ramesh Karri, and Farshad Khorrami are with the Department of ECE, NYU Tandon School of Engineering, Brooklyn, NY 11201 USA (e-mail: [email protected]; [email protected]; [email protected]; [email protected]).
Publisher Copyright:
© 1982-2012 IEEE.
PY - 2020/11
Y1 - 2020/11
N2 - We demonstrate a novel technique that employs transistor short-term aging effects in integrated circuits (ICs) to detect hardware Trojans in embedded systems. In advanced technology nodes (≤ 45 nm), voltage scaling in combination with short-term aging opens doors for short-term degradations. The induced short-term degradations result in dynamic variation of delays along various paths within the IC. Aging degradation generated under fast voltage switching from high to low results in bit errors at the circuit output. Our experiments use short-term aging-aware standard cell libraries to show the effectiveness of short-term aging to detect hardware Trojans. We extract a rich set of features that capture bit error patterns at the outputs of the IC. We use a one class SVM-based classifier that uses these features to learn the distribution of bit errors at the outputs of a clean IC. We discern the deviation in the pattern of bit errors due to a Trojan in the IC from the baseline distribution. To reiterate, the method uses the model of a clean IC. Furthermore, it is robust against chip-to-chip variations. We illustrate the technique on six Trojans from Trust-Hub spanning two cryptographic chips and an embedded PIC microcontroller. Our approach detects Trojans with an accuracy ≥ 95%. It is easier to detect Trojans in an optimized-netlist circuit as more paths are close to the critical path. Even when the circuit is not optimized (i.e., when very few paths are close to the critical path), short-term aging plus mild overclocking can detect Trojans with high accuracy.
AB - We demonstrate a novel technique that employs transistor short-term aging effects in integrated circuits (ICs) to detect hardware Trojans in embedded systems. In advanced technology nodes (≤ 45 nm), voltage scaling in combination with short-term aging opens doors for short-term degradations. The induced short-term degradations result in dynamic variation of delays along various paths within the IC. Aging degradation generated under fast voltage switching from high to low results in bit errors at the circuit output. Our experiments use short-term aging-aware standard cell libraries to show the effectiveness of short-term aging to detect hardware Trojans. We extract a rich set of features that capture bit error patterns at the outputs of the IC. We use a one class SVM-based classifier that uses these features to learn the distribution of bit errors at the outputs of a clean IC. We discern the deviation in the pattern of bit errors due to a Trojan in the IC from the baseline distribution. To reiterate, the method uses the model of a clean IC. Furthermore, it is robust against chip-to-chip variations. We illustrate the technique on six Trojans from Trust-Hub spanning two cryptographic chips and an embedded PIC microcontroller. Our approach detects Trojans with an accuracy ≥ 95%. It is easier to detect Trojans in an optimized-netlist circuit as more paths are close to the critical path. Even when the circuit is not optimized (i.e., when very few paths are close to the critical path), short-term aging plus mild overclocking can detect Trojans with high accuracy.
KW - Hardware security
KW - Trojan detection
KW - machine learning (ML)
KW - short-term aging
KW - voltage scaling
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U2 - 10.1109/TCAD.2020.3012649
DO - 10.1109/TCAD.2020.3012649
M3 - Article
AN - SCOPUS:85096032164
SN - 0278-0070
VL - 39
SP - 3519
EP - 3530
JO - IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
JF - IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
IS - 11
M1 - 9211451
ER -