Spectral graph theoretic analysis of large-scale network topologies with applications to secure data transmission

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.