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

Graph-driven side-channel attack detection using ai and discrete math

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pp. 807–815Vol. 29Issue 2-BFebruary 2026DOI: 10.47974/JDMSC-2523 Crossmark XML
Received:
07 May 2025
Published Online:
12 Feb 2026
Article type:
Research Article
Language:
EN
Article no.:
JDMSC-2523
Pages:
807–815

Abstract

By combining graph-theoretic modelling and artificial intelligence methods, this study shows a new way to find side-channel attacks.  Data from the side channels is shown as graphs that show the connections and patterns that could be used in an attack.  Feature extraction techniques are used on these graph models to show off unique attack patterns.  Graph Neural Networks (GNNs) and Support Vector Machines (SVMs) are two advanced AI algorithms that are designed and trained to accurately find side-channel intrusions.  Discrete mathematics ideas like vector measure spaces and orthogonal sequences are used in the suggested method to make identification more reliable.  Evaluation measures and validation methods are used to check the accuracy and dependability of detection, showing that the framework works well at finding both simple and complex side-channel dangers.

Keywords

Subject Classifications

68R0168M25

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