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MacLeR: Machine Learning-based Run-Time Hardware Trojan Detection in Microprocessors in Resource-Constrained IoT Edge Devices
11:45am - 12:30pm, Sep 21, 2020
CASES at Embedded System Week

Speaker:
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Faiq Khalid

Authors:
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Faiq Khalid, Technische Universität Wien
Syed Rafay Hasan, Tennessee Tech Univesity, USA
Sara Zia, NUST SEECS, Pakistan
Osman Hasan, NUST SEECS, Pakistan
Falah Awwad, United Arab Emirates University
Muhammad Shafique, Technische Universität Wien, New York University Abu-Dhabi

Abstract:
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Traditional learning-based approaches for run-time Hardware Trojan detection require complex and expensive on-chip data acquisition frameworks and thus incur high area and power overhead. To address these challenges, we propose to leverage the power correlation between the executing instructions of a microprocessor to establish a machine learning-based run-time Hardware Trojan (HT) detection framework, called MacLeR. To reduce the overhead of data acquisition, we propose a single power-port current acquisition block using current sensors in time-division multiplexing, which increases accuracy while incurring reduced area overhead. We have implemented a practical solution by analyzing multiple HT benchmarks inserted in the RTL of a system-on-chip (SoC) consisting of four LEON3 processors integrated with other IPs like vga_lcd, RSA, AES, Ethernet, and memory controllers.

Our experimental results show that compared to state-of-the-art HT detection techniques, MacLeR achieves 10% better HT detection accuracy (i.e., 96.256%) while incurring a 7x reduction in area and power overhead (i.e., 0.025\% of the area of the SoC and

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