MacLeR: Machine Learning-Based Runtime Hardware Trojan Detection in Resource-Constrained IoT Edge Devices

Faiq Khalid, Syed Rafay Hasan, Sara Zia, Osman Hasan, Falah Awwad, Muhammad Shafique

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)


Traditional learning-based approaches for runtime hardware Trojan (HT) 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 (ML)-based runtime 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, such as 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 $7\times $ reduction in area and power overhead (i.e., 0.025% of the area of the SoC and < 0.07% of the power of the SoC). In addition, we also analyze the impact of process variation (PV) and aging on the extracted power profiles and the HT detection accuracy of MacLeR. Our analysis shows that variations in fine-grained power profiles due to the HTs are significantly higher compared to the variations in fine-grained power profiles caused by the PVs and aging effects. Moreover, our analysis demonstrates that on average, the HT detection accuracy drops in MacLeR is less than 1% and 9% when considering only PV and PV with worst case aging, respectively, which is $\approx 10\times $ less than in the case of the state-of-the-art ML-based HT detection technique.

Original languageEnglish
Article number9211562
Pages (from-to)3748-3761
Number of pages14
JournalIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Issue number11
Publication statusPublished - Nov 2020


  • Hardware security
  • LEON3
  • hardware trojans
  • machine learning
  • microprocessor
  • power profiling

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering


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