
This dataset is part of the SETS-THIRAL dataset repository, which comprises multiple datasets suitable for performing AI-assisted Side-Channel Analysis (SCA). It consists of Power consumption traces obtained from an unprotected PRINCE lightweight cryptographic implementation executed on Xmega microcontroller platform.
The traces were obtained by measuring the real-time power consumption of an PRINCE encryption process during its first round. These measurements are highly suitable for Side-Channel Analysis (SCA), enabling cryptographic key recovery through both statistical techniques and deep learning–based approaches. Power consumption data were collected using a ChipWhisperer CW1200 platform connected to an Xmega microcontroller. Trigger-based synchronization is used during acquisition process to align the captured traces with encryption operations. After collection, the traces were formatted and annotated with appropriate labels to support side-channel analysis experiments and AI model development. The dataset is provided in HDF5 (.h5) format and is divided into Profiling_traces and Attack_traces groups. The Profiling_traces group consists of 100,000 power traces, each containing 5,000 sample points along with a metadata structured array containing the corresponding plaintext, key, and ciphertext values and a label array. The Attack_traces group contains 20,000 power traces together with the corresponding metadata structured array. The labels correspond to the most significant nibble (MSB nibble) of the first-round S-box output for byte 0 of the plaintext. Label generation uses plaintext byte 0 (PT[0]) and the 128-bit PRINCE master key. The master key is divided into two 64-bit subkeys, K0 and K1. A derived key byte is obtained from the XOR of K0 and K1, and the byte at position 0 (K0 XOR K1)[0], is used in the first-round computation. The intermediate value is computed by XORing PT[0] with (K0 XOR K1)[0], and the result is passed through the PRINCE S-box. The resulting 8-bit S-box output is divided into its most significant nibble (bits 7-4) and least significant nibble (bits 3-0). The most significant nibble is used as the class label, resulting in 16 classes (0-15).
This Dataset Is Developed For Research And Educational Purposes In Side-channel Analysis (Sca). It Provides Labelled Power Traces Captured From Cryptographic Computations And Can Be Used To Develop, Evaluate And Benchmark Classical And Ai-assisted Attack Methodologies. The Dataset Also Facilitates The Study Of Leakage Characteristics, Feature Extraction Techniques, Model Interpretability And The Evaluation Of Cryptographic Countermeasures.
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