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A Deep-Learning Technique to Locate Cryptographic Operations in Side-Channel Traces

Giuseppe Chiari, Davide Galli, Francesco Lattari, Matteo Matteucci, Davide Zoni

TL;DR

This paper presents a novel deep-learning technique to locate the time instant in which the target computed cryptographic operations are executed in the side-channel trace and validated it through a successful attack against a variety of unprotected and protected cryptographic primitives that have been executed on an FPGA-implemented system-on-chip featuring a RISC- V CPU.

Abstract

Side-channel attacks allow extracting secret information from the execution of cryptographic primitives by correlating the partially known computed data and the measured side-channel signal. However, to set up a successful side-channel attack, the attacker has to perform i) the challenging task of locating the time instant in which the target cryptographic primitive is executed inside a side-channel trace and then ii)the time-alignment of the measured data on that time instant. This paper presents a novel deep-learning technique to locate the time instant in which the target computed cryptographic operations are executed in the side-channel trace. In contrast to state-of-the-art solutions, the proposed methodology works even in the presence of trace deformations obtained through random delay insertion techniques. We validated our proposal through a successful attack against a variety of unprotected and protected cryptographic primitives that have been executed on an FPGA-implemented system-on-chip featuring a RISC-V CPU.

A Deep-Learning Technique to Locate Cryptographic Operations in Side-Channel Traces

TL;DR

This paper presents a novel deep-learning technique to locate the time instant in which the target computed cryptographic operations are executed in the side-channel trace and validated it through a successful attack against a variety of unprotected and protected cryptographic primitives that have been executed on an FPGA-implemented system-on-chip featuring a RISC- V CPU.

Abstract

Side-channel attacks allow extracting secret information from the execution of cryptographic primitives by correlating the partially known computed data and the measured side-channel signal. However, to set up a successful side-channel attack, the attacker has to perform i) the challenging task of locating the time instant in which the target cryptographic primitive is executed inside a side-channel trace and then ii)the time-alignment of the measured data on that time instant. This paper presents a novel deep-learning technique to locate the time instant in which the target computed cryptographic operations are executed in the side-channel trace. In contrast to state-of-the-art solutions, the proposed methodology works even in the presence of trace deformations obtained through random delay insertion techniques. We validated our proposal through a successful attack against a variety of unprotected and protected cryptographic primitives that have been executed on an FPGA-implemented system-on-chip featuring a RISC-V CPU.
Paper Structure (12 sections, 1 equation, 3 figures, 2 tables)

This paper contains 12 sections, 1 equation, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Methodology
  4. Dataset Creation
  5. Convolutional Neural Network
  6. Sliding Window Classification
  7. Segmentation
  8. Experimental Evaluation
  9. Experimental Setup: Hardware and Software
  10. CNN Evaluation
  11. The Complete Attack Flow
  12. Conclusions

Figures (3)

  • Figure 1: Overview of the proposed pipeline for locating cryptographic operations, divided into training and inference phases. In the Segmentation block, Th and MF identify the threshold and median filter procedures.
  • Figure 2: Employed 1D CNN architecture. The network is an adaptation of the known ResNet resnet for 2D image classification.
  • Figure 3: Test confusion matrices for the different cryptosystems affected by RD-4 random delay.