정상윤, 김희석, 진성현,

Vol. 34, No. 3, pp. 379-392, 6월. 2024
10.13089/JKIISC.2024.34.3.379, Full Text:
Keywords: Side-Channel Analysis, compression, Autoencoder, Deep Learning
Abstract

With the rapid increase in data, saving storage space and improving the efficiency of data transmission have become critical issues, making the research on the efficiency of data compression technologies increasingly important. Lossless algorithms can precisely restore original data but have limited compression ratios, whereas lossy algorithms provide higher compression rates at the expense of some data loss. There has been active research in data compression using deep learning-based algorithms, especially the autoencoder model. This study proposes a new side-channel analysis data compressor utilizing autoencoders. This compressor achieves higher compression rates than Deflate while maintaining the characteristics of side-channel data. The encoder, using locally connected layers, effectively preserves the temporal characteristics of side-channel data, and the decoder maintains fast decompression times with a multi-layer perceptron. Through correlation power analysis, the proposed compressor has been proven to compress data without losing the characteristics of side-channel data.

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