Non-Profiled Side-Channel Attack Based on Deep Learning Using Picture Trace
| Autor: | Dong-Guk Han, Yoo-Seung Won, Dirmanto Jap, Jong-Yeon Park, Shivam Bhasin |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
050101 languages & linguistics
General Computer Science Computer science Computer science and engineering::Information systems::Information systems applications [Engineering] 02 engineering and technology Machine learning computer.software_genre Field (computer science) multi-layer perceptron Deep Learning 0202 electrical engineering electronic engineering information engineering Entropy (information theory) 0501 psychology and cognitive sciences General Materials Science Side channel attack Entropy (energy dispersal) Binarized Neural Network Binarized neural network TRACE (psycholinguistics) Contextual image classification Artificial neural network business.industry Deep learning 05 social sciences General Engineering deep learning non-profiled side-channel attack Key (cryptography) 020201 artificial intelligence & image processing lcsh:Electrical engineering. Electronics. Nuclear engineering Artificial intelligence business lcsh:TK1-9971 computer |
| Zdroj: | IEEE Access, Vol 9, Pp 22480-22492 (2021) |
| ISSN: | 2169-3536 |
| Popis: | Over the years, deep learning algorithms have advanced a lot and any innovation in the algorithms are demonstrated and benchmarked for image classification. Several other field including side-channel analysis (SCA) have recently adopted deep learning with great success. In SCA, the deep learning algorithms are typically working with 1-dimensional (1-D) data. In this work, we propose a unique method to improve deep learning based side-channel analysis by converting the measurements from raw-trace of 1-dimension data based on float or byte data into picture-formatted trace that has information based on the data position. We demonstrate why 'Picturization' is more suitable for deep learning and compare how input and hidden layers interact for each raw (1-D) and picture form. As one potential application, we use a Binarized Neural Network (BNN) learning method that relies on a BNN's natural properties to improve variables. In addition, we propose a novel criterion for attack success or failure based on statistical confidence level rather than determination of a correct key using a ranking system. Published version |
| Databáze: | OpenAIRE |
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