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MacLeR: Machine Learning-based Run-Time Hardware Trojan Detection in Resource-Constrained IoT Edge Devices
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems ( IF 2.9 ) Pub Date : 2020-11-01 , DOI: 10.1109/tcad.2020.3012236
Faiq Khalid , Syed Rafay Hasan , Sara Zia , Osman Hasan , Falah Awwad , Muhammad Shafique

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.

中文翻译:

MacLeR:资源受限物联网边缘设备中基于机器学习的运行时硬件木马检测

传统的基于学习的运行时硬件木马 (HT) 检测方法需要复杂且昂贵的片上数据采集框架,因此会导致高面积和功耗开销。为了应对这些挑战,我们建议利用微处理器执行指令之间的功率相关性来建立基于机器学习 (ML) 的运行时 HT 检测框架,称为 MacLeR。为了减少数据采集的开销,我们提出了一个在时分复用中使用电流传感器的单电源端口电流采集模块,它提高了精度,同时减少了面积开销。我们通过分析插入到片上系统 (SoC) 的 RTL 中的多个 HT 基准测试来实施一个实用的解决方案,该片上系统由四个与其他 IP 集成的 LEON3 处理器组成,例如 vga_lcd、RSA、AES、以太网、和内存控制器。我们的实验结果表明,与最先进的 HT 检测技术相比,MacLeR 的 HT 检测精度提高了 10%(即 96.256%),同时使面积和功率开销减少了 7 美元(即 0.025%) SoC 的面积和 < SoC 功率的 0.07%)。此外,我们还分析了工艺变化 (PV) 和老化对 MacLeR 提取的功率曲线和 HT 检测精度的影响。我们的分析表明,与由 PV 和老化效应引起的细粒度功率分布的变化相比,由于 HT 引起的细粒度功率分布的变化明显更高。此外,我们的分析表明,平均而言,当仅考虑 PV 和具有最坏老化情况的 PV 时,MacLeR 中的 HT 检测精度下降分别小于 1% 和 9%,
更新日期:2020-11-01
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