TARU PUBLICATIONS
Journal of Discrete Mathematical Sciences and Cryptography cover
Hybrid ·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
submissions@tarupublications.com
Open Access Research Article

Designing robust cybersecurity measures using optimization techniques in enterprise information

, , * , , ,

* Corresponding author · click or hover a name for details

pp. 1067–1075Vol. 29Issue 2-BFebruary 2026DOI: 10.47974/JDMSC-2646 Crossmark XML
Received:
07 May 2025
Published Online:
18 Feb 2026
Article type:
Research Article
Language:
EN
Article no.:
JDMSC-2646
Pages:
1067–1075

Abstract

Businesses are facing new online dangers because their IT systems are getting more complicated. To protect themselves, they need strong, flexible, and resource-efficient defences. This paper looks at how optimization methods can be used to make business security systems stronger. Linear and nonlinear optimization models help you make structured decisions that minimize costs and risks, and metaheuristic techniques like genetic algorithms, particle swarm optimization, and ant colony optimization let you deal with threats in a way that adapts to them. It is possible to combine goal functions and limits that show how information flows in a business using maths. The results show that methods based on optimization improve the reliability, efficiency, and flexibility of defending sensitive business information assets.

Keywords

Subject Classifications

46N1013P25

References

[1] K. Sathupadi, “A hybrid deep learning framework combining on-device and cloud-based processing for cybersecurity in mobile cloud environments,” Int. J. Inf. Cybersecur., vol. 7, pp. 61–80 (2023).
[2] S. M. Zohaib, S. M. Sajjad, Z. Iqbal, M. Yousaf, M. Haseeb, and Z. Muhammad, “Zero Trust VPN (ZT-VPN): A Systematic Literature Review and Cybersecurity Framework for Hybrid and Remote Work,” Information, vol. 15, pp. 734 (2024).
[3] S. Rose, O. Borchert, S. Mitchell, and S. Connelly, Zero Trust Architecture. Gaithersburg, MD, USA: National Institute of Standards and Technology (2020).
[4] R. Chandramouli, Guide to a Secure Enterprise Network Landscape. Gaithersburg, MD, USA: US Department of Commerce, National Institute of Standards and Technology (2022).
[5] S. S. Wang and U. Franke, “Enterprise IT service downtime cost and risk transfer in a supply chain,” Oper. Manag. Res., vol. 13, pp. 94–108 (2020).
[6] M. R. Mollahoseini Ardakani, S. M. Hashemi, and M. Razzazi, “A cloud-based solution/reference architecture for establishing collaborative networked organizations,” J. Intell. Manuf., vol. 30, pp. 2273–2289 (2019).
[7] D. Jia and Z. Wu, “Enterprise collaborative integrated management system based on IoT cloud technology,” Mob. Inf. Syst., vol. 2022, Article ID 6098201 (2022).
[8] F. Li and G. Xu, “AI-driven customer relationship management for sustainable enterprise performance,” Sustain. Energy Technol. Assess., vol. 52, pp. 102103 (2022).
[9] A. Papaioannou, A. Dimara, C. S. Kouzinopoulos, S. Krinidis, C. N. Anagnostopoulos, D. Ioannidis, and D. Tzovaras, “LP-OPTIMA: A framework for prescriptive maintenance and optimization of IoT resources for low-power embedded systems,” Sensors, vol. 24, pp. 2125 (2024).
[10] A. Dimara, V. G. Vasilopoulos, A. Papaioannou, S. Angelis, K. Kotis, C. N. Anagnostopoulos, S. Krinidis, D. Ioannidis, and D. Tzovaras, “Self-healing of semantically interoperable smart and prescriptive edge devices in IoT,” Appl. Sci., vol. 12, pp. 11650 (2022).
[11] N. N. H. Adenan, A. Nitaj, M. R. K. Ariffin, and N. A. Abu, “Cryptanalysis of a cubic Pell variant of RSA with primes sharing least significant bits,” J. Inf. Optim. Sci., vol. 45, no. 5, pp. 1263–1280 (2024).
[12] S. Saxena, M. Z. Khan, and R. Singh, “Green computing: An era of energy saving computing of cloud resources,” Int. J. Math. Sci. Comput., vol. 7, pp. 42–48 (2021).
[13] W. N. Anandpwar, S. M. Barhate, and M. P. Dhore, “Designing an efficient machine learning-based intrusion detection system for enhanced cyber security in modern networks,” Int. J. Adv. Comput. Theory Eng., vol. 14, no. 1, pp. 70–75 (Apr. 2025).

Views: 164Downloads: 47Citations: 0