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DPA tools benchmarking

This is so far a very basic benchmarking of the computational part of differential power analysis implemented by existing tools. Only runtime to process a traceset is measured.

The motivation for performance benchmarking of DPA tooling is twofold:

  1. Side channel analysis is an iterative experimental process where fast feedback loop from experiment to analyst is essential
  2. In many settings the ultimate attack metric is not just the number of traces but the time required for key recovery

Slides with a bit more details (note that the figures there may be outdated compared to this repo):

To clone this repo with the included large datasets you will need Git-LFS. Without Git-LFS, only pointers to large datasets will be cloned.

Maintainers: Cees-Bart Breunesse, Ilya Kizhvatov

Tools and configuration

Standard install described in the repositories/manuals of these tools.

For ChipWhisperer, the C-accelerated CPA implementation has to be built manually in software/chipwhisperer/analyzer/attacks/cpa_algorithms/c_accel.

As not all of the tools are scriptable, the benchmarking is only minimally automated.

Test case 1: CPA on AES-128

Traceset: 100K traces, 512 float32 samples/trace, 32 byte data

Running

Convert the traceset into different formats

$ cd testcase01
$ ./prepareTraceset.sh

Run measurements for main Jlsca variants and Daredevil. The timings are output at the end. You may want to adjust max memory and #threads settings for Daredevil in its config file.

$ ./run_jlsca_condavg[_hw].sh
$ ./run_jlsca_inccpa[_hw].sh
$ ./run_daredevil[_hw].sh

ChipWhisperer and Inspector are run from the GUI. Inspector configuration settings and ChipWhisperer attack script attack_cpa_caccel.py are included in the repository.

Results all-bit CPA

Tool Laptop Desktop Ratio
Daredevil 980 392 2.5
Inspector 847 377 2.3
Jlsca, incremental correlation 381 189 2.0
Jlsca, conditional averaging 20 12 1.6

ChipWhisperer does not include the all-bit CPA by default.

Results classical CPA in the Hamming weight model

Tool Laptop Desktop Ratio
ChipWhisperer 1109 840 1.3
Daredevil 128 54 2.4
Inspector 124 73 1.7
Jlsca, incremental correlation 60 32 2.2
Jlsca, conditional averaging 19 11 1.7

Remarks

  • Runtime shown in seconds
  • Ratio column shows the speedup factor from moving to a more powerful platform
  • Daredevil, Inspector and ChipWhisperer implement incremental correlation computation
  • For ChipWhisperer, the two available non-accelerated versions took on a laptop 1 hour 14 minutes (Progressive) and 2 hours (Simple).
  • Number of threads chosen for fastest execution:
    • for Daredevil, Jlsca and Inspector, number of physical cores x2 (assuming hyperthreading is on)
    • for Jlsca, for conditional averaging the number of threads does not matter
  • Memory:
    • in Daredevil config, max memory is set to physical RAM size minus 1 GB
  • Windows vs Linux:
    • Jlsca in Windows similar performance to Linux
    • Daredevil on Windows (cygwin build) 30% slower than on Linux

Platforms

Laptop: i5-3230M 2.6 GHz (dual-core), 4 GB 1600 MHz DDR3, HDD, Ubuntu 16.04 x64 4.4.0-116 / Windows 7 x64

Desktop: i7-6700 3.4 GHz (quad-core), 64 GB 2133 MHz DDR4, HDD, Ubunutu 16.04 x64 4.4.0-116 / Windows 7 x64

TurboBoost is on, normal room temperature.

Tool versions:

  • Daredevil 897f602, built with g++ 5.4.0-6ubuntu1~16.04.5 and run under Linux
  • Jlsca 6fa3f9bf, run with julia 0.6.2 under Linux
  • ChipWhisperer 5ba1b1c, C-accel built with g++ 5.4.0-6ubuntu1~16.04.5, run with Python 2.7.12 under Linux. In later commits the C-accelerated attack segfaults.
  • Inspector 4.12, run under Windows

Note that the same Jlsca code can run (with minimal additions) on a cluster of machines. This is not included here as it is quite a different setting, but described separately in this tutorial.

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Benchmarking of various tooling for differential power analysis (computational part)

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