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Vulnerability analysis of Salsa20 : differential analysis and deep learning analysis of Salsa20

Knutson, Paul
Master thesis
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URI
https://hdl.handle.net/11250/2730004
Date
2020
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  • Master in Systems Engineering with Embedded Systems [5]
Abstract
This work attempts to address the research question of how secure the current

solutions in lightweight cryptography are, and speci_cally, if Salsa20 is a

su_ciently secure algorithm for its intended purposes.

We perform a state of the art survey on the current landscape of lightweight

cryptography and a survey of the cryptanalysis most relevant to these kinds

of crypto systems. We take a closer look at the ARX-based stream cipher

Salsa20, analyse its security and give recommendation based on the results.

We implement two analyses against both Salsa20 and one of its code components,

the quarter-round function. While breaking the quarter-round may

not be useful for breaking Salsa20, it gives us an idea of the viability of the

analysis. The two analysis methods are:

1. Di_erential analysis using the Hamming distance.

We found that the quarter-round, when treated like an encryption algorithm,

had an insu_cient avalanche e_ect and is easily distinguishable

from random noise for chosen plaintexts. We could not _nd any indication

the full Salsa20 algorithm su_er from these e_ects.

2. Deep learning-based analysis using a context aggregation network.

This analysis used images (some generated from random noise, some

actual images), encrypted them, and tested if the context aggregation

network (CAN) was able to learn and reconstruct parts of the original

images or plaintexts. The results indicated this method is not viable

against either Salsa20 nor its quarter-round function.

We therefore conclude that these forms of analysis does not seem e_ective

against Salsa20.
Publisher
Universitetet i Sørøst-Norge
Copyright
© 2020 Paul Knutson

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