AutoCPS: Control Software Dataset Generation for Semantic Reverse Engineering

Binary analysis of closed-source, low-level, and embedded systems software has emerged at the heart of cyber-physical vulnerability assessment of third-party or legacy devices in safety-critical systems. In particular, recovering the semantics of the source algorithmic implementations enables analysts to understand the context of a particular binary program snippet. However, experimentation and evaluation of binary analysis techniques on real-world embedded cyber-physical systems are limited to domain-specific testbeds with a low number of use cases–insufficient to support emerging data-driven techniques. Moreover, the use cases rarely have the source mathematical expressions, algorithms, and compiled binaries. In this paper, we present AutoCPS, a framework for generating a large corpus of control systems binaries along with their source algorithmic expressions and source code. AutoCPS enables researchers to tune the control system binary data generation by varying different permutations of cyber-physical modules, e.g., the underlying control algorithm, while ensuring a semantically valid binary. We initially constrain AutoCPS to the flight software domain and generate over 4000 semantically different control systems source representations, which are then used to generate hundreds of thousands of binaries. We describe current and future use cases of AutoCPS towards cyber-physical vulnerability assessment of safety-critical systems.