深度学习
计算机科学
人工智能
机器学习
跟踪(心理语言学)
人工神经网络
原始数据
领域(数学)
排名(信息检索)
数据挖掘
模式识别(心理学)
数学
语言学
哲学
程序设计语言
纯数学
作者
Yoo-Seung Won,Dezhi Han,Dirmanto Jap,Shivam Bhasin,Jong Yeon Park
出处
期刊:IEEE Access
[Institute of Electrical and Electronics Engineers]
日期:2021-01-01
卷期号:9: 22480-22492
被引量:13
标识
DOI:10.1109/access.2021.3055833
摘要
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.
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