TARU PUBLICATIONS
Author

Priya Mathur

Department of Mathematics, Poornima Institute of Engineering & Technology, Jaipur, Rajasthan, 302022, India

Published papers
4
Citations
0
Views
279
Downloads
143

Publications

4 papers
Open Access Research Article·pp. 3305–3313·Vol. 29, Issue 8Aug 2026

A discrete graph-theoretic spectral framework for analyzing classical cipher security

Priya Mathur

Published Online: 14 Aug 2026DOI: 10.47974/JDMSC-2817

In this paper: This paper proposes a spectral graph-theoretic framework for cryptanalysis using Cipher Transition Graphs (CTGs) and metrics such as eigenvalues, spectral gap, entropy, clustering, and path length. Re...

AbstractReferencesFull Text PDF (755 KB)Views: 44Downloads: 32Citations: 0
Open Access Research Article·pp. 3219–3227·Vol. 29, Issue 8Aug 2026

Security analysis of cryptographic Boolean functions using a discrete mathematical model and machine learning

Priya Mathur, Kusum Lata Jain, Meenakshi Nawal, Neeraj, Amit Kumar Gupta

Published Online: 14 Aug 2026DOI: 10.47974/JDMSC-2748

In this paper: Cryptographic security of symmetric primitives relies on the discrete mathematical properties of Boolean functions and S-boxes. While machine learning (ML) has shown effectiveness in cryptanalysis, ma...

AbstractReferencesFull Text PDF (570 KB)Views: 56Downloads: 41Citations: 0
Open Access Research Article·pp. 3279–3294·Vol. 29, Issue 8Aug 2026

A graph-theoretic discrete mathematical model for cryptanalysis of classical ciphers using spectral transition network analysis

Priya Mathur, Amit Kumar Gupta

Published Online: 14 Aug 2026DOI: 10.47974/JDMSC-2815

In this paper: Cryptanalysis plays an essential role in evaluating the robustness and reliability of cryptographic systems used for secure communication. Traditional cryptanalysis methods primarily rely on statistic...

AbstractReferencesFull Text PDF (607 KB)Views: 51Downloads: 29Citations: 0
Open Access Research Article·pp. 3249–3257·Vol. 29, Issue 8Aug 2026

Spectral security analysis of cryptographic Boolean functions using discrete mathematical modelling and machine learning

Priya Mathur, Pradeep Gupta, K. Nandhini, Lipika Goel, Satpal Singh Kushwaha, Amit Kumar Gupta

Published Online: 14 Aug 2026DOI: 10.47974/JDMSC-2812

In this paper: This study investigates how the structural and spectral properties of Boolean functions influence their susceptibility to machine learning–based cryptanalysis. A comprehensive framework is proposed, c...

AbstractReferencesFull Text PDF (626 KB)Views: 128Downloads: 41Citations: 0