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Authors: Anton V. Konygin 1 ; 2 ; Andrey Kopnin 1 ; Ilya Mezentsev 1 and Alexandr Pankratov 1

Affiliations: 1 SKB Kontur, Russia ; 2 N.N. Krasovskii Institute of Mathematics and Mechanics, Russia

Keyword(s): Leaked Secrets, Source Code, Machine Learning, Security.

Abstract: Leaked secrets in source code lead to information security problems. It is important to find sensitive information in the repository as early as possible and neutralize it. By now, there are many different approaches to leaked secret detection without human intervention. Often, these are heuristic algorithms using regular expressions. Recently, more and more approaches based on machine learning have appeared. Nevertheless, the problem of detecting secrets in the code remains relevant since the available approaches often give a large number of false positives. In this paper, we propose an approach to leaked secret detection in source code based on machine learning using bigrams. This approach significantly reduces the number of false positives. The model showed a false positive rate of 2.4% and false negative rate of 1.9% on test dataset.

CC BY-NC-ND 4.0

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Paper citation in several formats:
V. Konygin, A.; Kopnin, A.; Mezentsev, I. and Pankratov, A. (2023). Using Bigrams to Detect Leaked Secrets in Source Code. In Proceedings of the 18th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE; ISBN 978-989-758-647-7; ISSN 2184-4895, SciTePress, pages 589-596. DOI: 10.5220/0011983600003464

@conference{enase23,
author={Anton {V. Konygin}. and Andrey Kopnin. and Ilya Mezentsev. and Alexandr Pankratov.},
title={Using Bigrams to Detect Leaked Secrets in Source Code},
booktitle={Proceedings of the 18th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE},
year={2023},
pages={589-596},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011983600003464},
isbn={978-989-758-647-7},
issn={2184-4895},
}

TY - CONF

JO - Proceedings of the 18th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE
TI - Using Bigrams to Detect Leaked Secrets in Source Code
SN - 978-989-758-647-7
IS - 2184-4895
AU - V. Konygin, A.
AU - Kopnin, A.
AU - Mezentsev, I.
AU - Pankratov, A.
PY - 2023
SP - 589
EP - 596
DO - 10.5220/0011983600003464
PB - SciTePress

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