Beneath the potential benefits of the rapidly growing Internet of Things (IoT) technology lurk security risks. In this article, we propose a hardware-based generic framework for IoT workload forensics, an infrastructural technique to securely monitor and ensure delivered IoT services in accordance with specifications and regulatory compliance. In particular, this technique identifies digital workloads being executed in real time through dynamic program behavior modeling based on architecture-level data, fulfilled by dedicated machine learning hardware, without the intervention of high-level software, e.g., the OS and/or the hypervisor. In contrast to the conventional software-based solutions, whose effectiveness may be undermined by software attacks, and which introduce significant runtime overhead, a hardware-based framework enables a secure, prompt and non-intrusive solution. The proposed framework was evaluated on Zedboard, a Zynq-7000 FPGA embedding an ARM Cortex-A9 core. Experimental results using Mibench workload benchmark reveal an average workload identification accuracy of 96.37 percent with insignificant area/power overhead.

中文翻译：

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用于实时物联网工作负载取证的基于硬件的架构中立框架

在快速增长的物联网 (IoT) 技术的潜在优势之下，隐藏着安全风险。在本文中，我们为 IoT 工作负载取证提出了一个基于硬件的通用框架，这是一种基础设施技术，可根据规范和法规遵从性安全地监控和确保交付的 IoT 服务。特别是，该技术通过基于架构级数据的动态程序行为建模实时识别正在执行的数字工作负载，由专用机器学习硬件实现，无需高级软件（例如操作系统和/或管理程序）的干预. 与传统的基于软件的解决方案相比，其有效性可能会受到软件攻击的影响，并且会引入大量的运行时开销，基于硬件的框架可以实现安全、迅速和非侵入性的解决方案。提议的框架在 Zedboard 上进行了评估，Zedboard 是一种嵌入 ARM Cortex-A9 内核的 Zynq-7000 FPGA。使用 Mibench 工作负载基准测试的实验结果表明，平均工作负载识别准确率为 96.37%，而面积/功率开销微不足道。