Ramsey theory applications in network security
*Madhumay SenCorresponding authorsenmadhumay@gmail.comDepartment of Computer Science & EngineeringAmity UniversityJaipur, Rajasthan, 303002, IndiaView full profile → , P. Ramya Krishnaramyakrishna@outlook.comDepartment of Electronics and Communication EngineeringPragati Engineering CollegeKakinada District, Andhra Pradesh, 533437, IndiaView full profile → , M. Thamizhsudarthamizhsudar@avit.ac.inDepartment of Humanities and Sciences (Mathematics Division)Aarupadai Veedu Institute of TechnologyVinayaka Mission’s Research Foundation (Deemed to be University)Paiyanoor, Chennai, Tamil Nadu, 603104, IndiaView full profile → , S. V. Panchamipanchami20@gmail.comDepartment of Electronics & Communication EngineeringVidyavardhaka College of EngineeringMysuru, Karnataka, 570002, IndiaView full profile → , Dharmesh Dhabliyadharmeshdhabliya@gmail.comDepartment of Information TechnologyVishwakarma Institute of TechnologyPune, Maharashtra, 411037, IndiaView full profile → , Nilam S. Patilnspatil@dypcoeakurdi.ac.inDepartment of Computer EngineeringD. Y. Patil College of EngineeringAkurdi, Pune, Maharashtra, 412101, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Apr 2026
- Published Online:
- 31 Oct 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JDMSC-3066
- Pages:
- 4473–4482
Abstract
Ramsey Theory is a strong tool of combinatorics of studying the patterns that will inevitably occur in complex networks and is of great importance in network security. The current paper introduces an analytical framework based on Ramsey, which can be used to identify the latent patterns, anomalies and adversarial structure of communication graphs. The solution offered includes machine learning models that have graph-based algorithms to detect and predict threats adaptively. The framework determines worst-case attack scenarios, and structural vulnerabilities by modeling the spread of vulnerabilities with Ramsey-critical subgraph. Experimental analysis shows that the detection (up to 94.6%) and false alarms are lower compared to the traditional graph algorithms. The limitations of the computational complexity and the issue of scalability, which provides optimized hybrid solutions are also discussed in the paper.
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References
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