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Evolving a Boolean Masked Adder Using Neuroevolution

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Attacks and Defenses for the Internet-of-Things (ADIoT 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13745))

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Abstract

The modular addition is a popular building block when designing lightweight ciphers. While algorithms mainly based on the addition can reach very high performance, masking their implementations results in a huge penalty. Since efficient protection against side-channel attacks is a requirement in lots of use cases, we focus on optimizing the Boolean masking of the modular addition. Contrary to recent related work, we target evolving a masked full adder instead of parts of a parallel prefix adder. We study how techniques typically found in neural network evolution and genetic algorithms can be adapted in order to help in evolving an efficiently masked adder. We customize a well-known neuroevolution algorithm, develop an optimized masked adder with our new approach and implement the ChaCha20 cipher on an ARM Cortex-M3 controller. We compare the performance of the protected neuroevolved implementation to solutions found by traditional search methods. Moreover, the leakage of our new solution is validated by a t-test conducted with a leakage simulator. We present under which circumstances our masked implementation outperforms related work and prove the feasibility of successfully using neuroevolution when searching for complex Boolean networks.

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Acknowledgements

This project is funded by the Bavarian State Ministry of Science and the Arts and coordinated by the Bavarian Research Institute for Digital Transformation (bidt). Furthermore, this research is supported by the BayWISS Consortium Digitization.

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Authors and Affiliations

  1. OTH Regensburg, Regensburg, Germany

    Sebastian Renner, Enrico Pozzobon & Jürgen Mottok

  2. Technical University of Munich, Munich, Germany

    Sebastian Renner

  3. University of West Bohemia, Pilsen, Czech Republic

    Enrico Pozzobon

Authors
  1. Sebastian Renner
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  2. Enrico Pozzobon
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  3. Jürgen Mottok
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Corresponding author

Correspondence to Sebastian Renner .

Editor information

Editors and Affiliations

  1. Hong Kong Polytechnic University, Hong Kong, China

    Wenjuan Li

  2. University of Nottingham, Nottingham, UK

    Steven Furnell

  3. Technical University of Denmark, Kongens Lyngby, Denmark

    Weizhi Meng

A ARM Assembly Implementation of the Shared Bitsliced 32-bit Adder

A ARM Assembly Implementation of the Shared Bitsliced 32-bit Adder

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Renner, S., Pozzobon, E., Mottok, J. (2022). Evolving a Boolean Masked Adder Using Neuroevolution. In: Li, W., Furnell, S., Meng, W. (eds) Attacks and Defenses for the Internet-of-Things. ADIoT 2022. Lecture Notes in Computer Science, vol 13745. Springer, Cham. https://doi.org/10.1007/978-3-031-21311-3_2

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  • DOI: https://doi.org/10.1007/978-3-031-21311-3_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21310-6

  • Online ISBN: 978-3-031-21311-3

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