
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 AES-128 cryptographic implementation executed on ATxmega microcontroller platform.
The traces were obtained by measuring the real-time power consumption of an AES-128 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 ATxmega 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 150,000 power traces, each containing 95,000 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. Labels are generated from the first-round S-box output of the AES-128 encryption algorithm. For each trace, plaintext byte 2 (PT[2]) is XORed with byte 2 of the secret key (K[2]). The resulting value is passed through the AES S-box, and the resulting 8-bit S-box output is used to derive the label. The labels correspond to the complete S-box output value, resulting in 256 possible classes ranging from 0 to 255.
This Dataset Is Generated 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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