Visible to the public Real-Time Instruction Execution Monitoring with Hardware-Assisted Security Monitoring Unit in RISC-V Embedded Systems

TitleReal-Time Instruction Execution Monitoring with Hardware-Assisted Security Monitoring Unit in RISC-V Embedded Systems
Publication TypeConference Paper
Year of Publication2022
AuthorsZhang, Zhun, Hao, Qiang, Xu, Dongdong, Wang, Jiqing, Ma, Jinhui, Zhang, Jinlei, Liu, Jiakang, Wang, Xiang
Conference Name2022 8th Annual International Conference on Network and Information Systems for Computers (ICNISC)
Date Publishedsep
KeywordsBehavioral sciences, Central Processing Unit, composability, embedded system, Embedded systems, hardware security, hardware Trojan attack, information assurance, intellectual property, Metrics, pubcrawl, Real-time Systems, resilience, Resiliency, RISC-V, Robustness, security, Security Monitoring Unit (SMU), Time to market
Abstract

Embedded systems involve an integration of a large number of intellectual property (IP) blocks to shorten chip's time to market, in which, many IPs are acquired from the untrusted third-party suppliers. However, existing IP trust verification techniques cannot provide an adequate security assurance that no hardware Trojan was implanted inside the untrusted IPs. Hardware Trojans in untrusted IPs may cause processor program execution failures by tampering instruction code and return address. Therefore, this paper presents a secure RISC-V embedded system by integrating a Security Monitoring Unit (SMU), in which, instruction integrity monitoring by the fine-grained program basic blocks and function return address monitoring by the shadow stack are implemented, respectively. The hardware-assisted SMU is tested and validated that while CPU executes a CoreMark program, the SMU does not incur significant performance overhead on providing instruction security monitoring. And the proposed RISC-V embedded system satisfies good balance between performance overhead and resource consumption.

DOI10.1109/ICNISC57059.2022.00048
Citation Keyzhang_real-time_2022