ML Encryption Dataset - for Classifying S-Box Generation Schemes in Embedded and IoT Cryptographic Sytems
Description
This dataset supports the study “Real-Time Machine-Learning Framework for Classifying S-Box Generation Schemes in Embedded and IoT Cryptographic Systems.” It includes 144 S-Box samples from 12 algorithmic families, covering Dynamic, RC4-derived, and chaotic-map (Henon, Logistic) designs. Each sample is labeled and processed into a feature vector of 40+ handcrafted attributes, capturing: Byte distribution statistics (mean, skewness, entropy, etc.) Transition-based features (first/second-order byte deltas) Cyclic and positional patterns (Fourier features, sine/cosine encodings) Information-theoretic measures (KLD, mutual information) The dataset is structured as a CSV file with one sample per row, including the label column (algorithm class) and all feature columns. It was generated using three independent 128-bit keys per algorithm and verified for class balance. This resource is suitable for: Benchmarking lightweight cryptographic classification models Studying S-Box fingerprinting in embedded systems Teaching machine-learning pipelines for security applications All data are anonymised and synthetically generated—no personal or hardware-identifiable content is included.
Files
Institutions
- Arab Academy for Science Technology and Maritime Transport