Microcontroller Program Control Flow as Space-Filling Curves for Anomaly Detection

This dataset comprises a structured collection of control flow representations derived from microcontroller program execution traces, visualized as space-filling curves. The dataset is organized into eight folders, each containing 1,000 NumPy arrays representing individual image samples. These samples are grouped into four logical categories, each corresponding to a different abstraction level of program trace data: (1) complete execution traces, (2) function-call-only traces, (3) conditional-statement-only traces, and (4) scaled and truncated function-call traces. Within each group, data is further divided into two subsets: benign program behavior and anomalous behavior, enabling supervised learning or anomaly detection research. By embedding control flow information into structured visual formats, this dataset facilitates novel applications of image-based machine learning techniques for embedded systems security and program behavior modeling.