Finally, as a practical framework, BinArm successfully detects 93 real-world CVE vulnerability entries, the majority of which have been confirmed, and the detection takes as little as 0.09 s per function on average. The experimental results also show that our detection engine can speed up the existing fuzzy matching approach by three orders of magnitude. Our experiments show that BinArm accurately identifies vulnerable functions with an average accuracy of 0.92. Specifically, the proposed engine takes a coarse-to-fine grained multi-stage function matching approach by (i) first filtering out dissimilar functions based on a group of heterogeneous features (ii) further filtering out dissimilar functions based on their execution paths and (iii) finally identifying candidate functions based on fuzzy graph matching. Then, we propose a multi-stage detection engine to minimize the computational cost of function matching and to address the scalability issue in handling large IED firmware. To this end, we build comprehensive databases of vulnerabilities and firmware that are both specific to smart grid IEDs. In this paper, we present BinArm, a scalable approach to detecting vulnerable functions in smart grid IED firmware mainly based on the ARM architecture. Although general-purpose techniques exist for vulnerability detection in firmware, they usually cannot meet the specific needs, e.g., they lack the domain knowledge specific to IED vulnerabilities, and they are often not efficient enough for handling larger firmware of IEDs.

Consequently, any vulnerabilities in IED firmware might greatly affect the security and functionality of the smart grid.

There is a widespread adoption of intelligent electronic devices (IEDs) in modern-day smart grid deployments.

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