Abstract
At CRYPTO’19, A. Gohr proposed neural distinguishers for the lightweight block cipher Speck32/64, achieving better results than the state-of-the-art at that point. However, the motivation for using that particular architecture was not very clear; therefore, in this paper, we study the depth-10 and depth-1 neural distinguishers proposed by Gohr [7] with the aim of finding out whether smaller or better-performing distinguishers for Speck32/64 exist. We first evaluate whether we can find smaller neural networks that match the accuracy of the proposed distinguishers. We answer this question in the affirmative with the depth-1 distinguisher successfully pruned, resulting in a network that remained within one percentage point of the unpruned network’s performance. Having found a smaller network that achieves the same performance, we examine whether its performance can be improved as well. We also study whether processing the input before giving it to the pruned depth-1 network would improve its performance. To this end, convolutional autoencoders were found that managed to reconstruct the ciphertext pairs successfully, and their trained encoders were used as a preprocessor before training the pruned depth-1 network. We found that, even though the autoencoders achieved a nearly perfect reconstruction, the pruned network did not have the necessary complexity anymore to extract useful information from the preprocessed input, motivating us to look at the feature importance to get more insights. To achieve this, we used LIME, with results showing that a stronger explainer is needed to assess it correctly.
Original language | English |
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Title of host publication | Applied Cryptography and Network Security - 20th International Conference, ACNS 2022, Proceedings |
Editors | Giuseppe Ateniese, Daniele Venturi |
Publisher | Springer |
Pages | 809-829 |
Number of pages | 21 |
ISBN (Print) | 978-3-031-09233-6 |
DOIs | |
Publication status | Published - 2022 |
Event | 20th International Conference on Applied Cryptography and Network Security, ACNS 2022 - Virtual, Online Duration: 20 Jun 2022 → 23 Jun 2022 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 13269 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 20th International Conference on Applied Cryptography and Network Security, ACNS 2022 |
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City | Virtual, Online |
Period | 20/06/22 → 23/06/22 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Keywords
- Feature importance
- Neural distinguisher
- Pruning
- Speck