Spectral graph theoretic analysis of large-scale network topologies with applications to secure data transmission
Jyoti L. Bangarejyoti.bangare@cumminscollege.inDepartment of Computer EngineeringMKSSS’s Cummins College of Engineering for WomenSavitribai Phule Pune UniversityPune, Maharashtra, 411052, IndiaView full profile → , *Nilofer KittadCorresponding authornilofer.attar@cumminscollege.inDepartment of Computer EngineeringMKSSS’s Cummins College of Engineering for WomenSavitribai Phule Pune UniversityPune, Maharashtra, 411052, IndiaView full profile → , Ashwini Rajurkarashwini.rajurkar@cumminscollege.inDepartment of Computer EngineeringMKSSS’s Cummins College of Engineering for WomenSavitribai Phule Pune UniversityPune, Maharashtra, 411052, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Mar 2026
- Published Online:
- 31 Oct 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-2506
- Pages:
- 3825–3836
Abstract
As the topology of the network grows, nodes fail and links become unavailable, and there are more malicious attacks, the communication networks must be able to transmit data securely and reliably. This study introduces a spectral graph theoretic approach to study the network topology and provides security-aware routing. Communication networks are modeled as weighted graphs, and the spectral properties are used to measure connectivity, find structurally important nodes, discover vulnerable links, and determine the robustness of networks. The proposed solution is to utilize the spectral information and path reliability, communication cost, and security risk along with the transmission delay to choose the robust transmission paths. Multiple network sizes, topology structures and under normal and attack conditions are considered in experimental evaluation. Results show that proposed routing framework can obtain better packet delivery, path reliability, secure transmission, and network resilience than shortest path, reliability-based routing, and traditional securityaware routing methods. The results highlight the potential of spectral graph analysis to give global information about a network in the context of adaptive routing decision-making, as well as the scalability of the method for enhancing secure data transmission in large-scale, dynamic and complex communication networks.
Keywords
Subject Classifications
References
[1] M. Alghamdi, E. F. Khairullah, and M. M. Al Mojamed, “LoRaWAN Performance Analysis for a Water Monitoring and Leakage Detection System in a Housing Complex,” Sensors, vol. 22, Art. no. 7188 (2022).
[2] W. Wang, X. Liu, J. Tang, N. Zhao, Y. Chen, Z. Ding, and X. Wang, “Beamforming and Jamming Optimization for IRS-Aided Secure NOMA Networks,” IEEE Trans. Wireless Commun., vol. 21, pp. 1557–1569 (2021).
[3] S. Singhai, P. Gautam, and J. S. Kushwah, “Posture Recognition in Exercise Frames via HOBV-Based Deep Sparse Learning,” J. Stat. Manag. Syst., vol. 29, no. 2, pp. 201–212 (2026), doi: 10.47974/JSMS-1565.
[4] X. Pang, N. Zhao, J. Tang, C. Wu, D. Niyato, and K. K. Wong, “IRS-Assisted Secure UAV Transmission via Joint Trajectory and Beamforming Design,” IEEE Trans. Commun., vol. 70, pp. 1140–1152 (2021).
[5] P. Khobragade, B. Thakran, P. K. Dhankar, P. Dasarwar, C. Vaidya, and K. Dhote, “Large Language Model-Driven Automated Malware Detection and Reverse Engineering for Secure Software Systems,” in Proc. 5th OPJU Int. Technol. Conf. (OTCON) Smart Comput. Innov. Adv. Ind. 5.0, Raigarh, India, pp. 1–6 (2026), doi: 10.1109/OTCON69332.2026.11629934.
[6] W. Jiang, B. Chen, J. Zhao, Z. Xiong, and Z. Ding, “Joint Active and Passive Beamforming Design for the IRS-Assisted MIMOME-OFDM Secure Communications,” IEEE Trans. Veh. Technol., vol. 70, pp. 10369–10381 (2021).
[7] J. Zhang, L. Pan, Q.-L. Han, C. Chen, S. Wen, and Y. Xiang, “Deep Learning-Based Attack Detection for Cyber-Physical System Cybersecurity: A Survey,” IEEE/CAA J. Autom. Sinica, vol. 9, pp. 377–391 (2022).
[8] J. P. S. Sundaram, W. Du, and Z. Zhao, “A Survey on LoRa Networking: Research Problems, Current Solutions, and Open Issues,” IEEE Commun. Surveys Tuts., vol. 22, pp. 371–388 (2020).
[9] D. Gibert, C. Mateu, and J. Planes, “The Rise of Machine Learning for Detection and Classification of Malware: Research Developments, Trends and Challenges,” J. Netw. Comput. Appl., vol. 153, Art. no. 102526 (2020).
[10] R. Prodanović, D. Rančić, I. Vulić, N. Zorić, D. Bogićević, G. Ostojić, S. Sarang, and S. Stankovski, “Wireless Sensor Network in Agriculture: Model of Cyber Security,” Sensors, vol. 20, Art. no. 6747 (2020).
[11] G. M. Upadhyay, N. Tewari, S. Shanker, A. K. Phogat, A. Misra, and T. Malche, “Analysis of the Robustness and Cryptographic Security of a DCT-Based Image Watermarking Scheme,” J. Discrete Math. Sci. Cryptogr., vol. 28, no. 8, pp. 3049–3058 (2025), doi: 10.47974/JDMSC-2466.
[12] S. Kim, K.-J. Park, and C. Lu, “A Survey on Network Security for Cyber-Physical Systems: From Threats to Resilient Design,” IEEE Commun. Surveys Tuts., vol. 24, pp. 1534–1573 (2022).
