TARU PUBLICATIONS
Journal of Discrete Mathematical Sciences and Cryptography cover
Open Access ·Peer-reviewed·ISSN (Online): 2169-0065·ISSN (Print): 0972-0529

Monthly Journal: Publishes theoretical and applied research in all areas of Discrete Mathematical Sciences, Cryptography, Combinatorics, Elliptic Curves and Information Security.

Issues up to 2022 co-published with and available at:Taylor & Francis Online
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Open Access Research Article

Elliptic curve-inspired graph neural networks for lightweight cryptographic verification

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pp. 1923–1934Vol. 28Issue 5-BAugust 2025DOI: 10.47974/JDMSC-2427 Crossmark XML
Received:
05 Nov 2024
Published Online:
30 Aug 2025
Article type:
Research Article
Language:
EN
Article no.:
JDMSC-2427
Pages:
1923–1934

Abstract

Elliptic curve cryptography (ECC) is considered as a secure communication encryption method due to its high security-per-bit and efficiency on low power devices. Yet, the validation of ECC-based tasks (e.g. signature validation, key agreement) can be a heavy overhead for lightweight sensors and devices. In this paper, we introduce a novel framework, CurveGNN which uses Graph Neural Network (GNN) with elliptic curve-based structures to quickly and heuristically verify cryptography attributes. By converting the ECC point addition relations and scalar multiplications to graph-structured data, CurveGNN can learn signatures (e.g., ECDSA) validity with marginal arithmetic overhead. We detail the graph construction, GNN architecture, and training strategy. We demonstrate via experiments on embedded-class microcontrollers that CurveGNN achieves more than 95% verification accuracy, with average energy saving over 40% for ECC verification compared to ECC verification, respectively. From our results we observed that CurveGNN is a feasible lightweight verifier in Internet of Things and edge-computing environments.

Keywords

Subject Classifications

68Q12

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