A Novel Side-Channel Archive Framework Using Deep Learning-Based Leakage Compression

Abstract

Side-channel analysis is one of the vulnerabilities in IoT's cryptographic systems. To present reliable side-channel analysis results, it is crucial to collect considerable amounts of power consumption traces. Handling large datasets in this context can be highly inefficient regarding data transmission and storage. In various fields, the importance of compression techniques for efficient data storage has increased significantly. Compression techniques for various types of datasets are often designed with consideration for their data characteristics, much like JPEG for images. However, despite its relatively low compression rates, side-channel analysis researchers commonly use Deflate for data compression due to its simplicity and universality. In this paper, we propose a novel side-channel data compression technique using autoencoders, which offers higher compression rates than Deflate while maintaining decompression times and achieving a fast compression time. Furthermore, our model preserves the characteristics of the side-channel throughout the compression and decompression processes. To verify this, we conducted experiments comparing the original data with traces decompressed data using our technique through correlation power analysis. The results confirmed that they exhibit similar correlation coefficients and identical peak positions, demonstrating the preservation of essential data properties.