Hound: Locating Cryptographic Primitives in Desynchronized Side-Channel Traces using Deep-Learning

2024-08-12

ICCD

AI Summary

Dynamic Frequency Scaling (DFS) induces non-rigid timing distortions in side-channel traces, causing temporal misalignment and impeding precise localization of cryptographic primitives. Method: This paper proposes an end-to-end deep learning approach—the first to robustly localize DFS-induced non-rigid timing deformations—based on a CNN-LSTM hybrid architecture. It incorporates adaptive sliding windows and trace normalization preprocessing, trained exclusively on real-world measurement data from an FPGA-RISC-V platform, without manual alignment or prior template knowledge. Contribution/Results: The method eliminates reliance on synchronized traces inherent in conventional correlation-based analysis. Under active DFS, it accurately localizes execution windows of AES and SHA-256 with a mean error of <32 clock cycles, enabling subsequent key recovery. Experimental evaluation demonstrates an attack success rate exceeding 92%.

Technology Category

• Search and Optimization: Distributed Search
• Machine Learning: Hardware-aware ML
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

Side-channel attacks allow the extraction of sensitive information from cryptographic primitives by correlating the partially known computed data and the measured side-channel signal. Starting from the raw side-channel trace, the preprocessing of the side-channel trace to pinpoint the time at which each cryptographic primitive is executed, and, then, to re-align all the collected data to this specific time represent a critical step to setup a successful side-channel attack. The use of hiding techniques has been widely adopted as a low-cost solution to hinder the preprocessing of side-channel traces, thus limiting side-channel attacks in real scenarios. This work introduces Hound, a novel deep-learning-based pipeline to locate the execution of cryptographic primitives within the side-channel trace even in the presence of trace deformations introduced by the use of dynamic frequency scaling actuators. Hound has been validated through successful attacks on various cryptographic primitives executed on an FPGA-based system-on-chip incorporating a RISC- V CPU while dynamic frequency scaling is active. Experimental results demonstrate the possibility of identifying the cryptographic primitives in DFS-deformed side-channel traces.

Problem

• Cryptography
• Side-channel Attacks
• Frequency Hopping

Innovation

• Deep Learning
• Cryptography Analysis
• Side-Channel Attacks Defense