Abstract
Spectral graph theory delves into graph properties through their spectral signatures. The eigenvalues of a graph’s Laplacian matrix are crucial for grasping its connectivity and overall structural topology. This research capitalizes on the inherent link between graph topology and spectral characteristics to enhance spectral graph analysis applications. In particular, such connectivity information is key to detect low signals that betray the occurrence of cyberattacks. This paper introduces SpectraTW, a novel spectral graph analysis methodology tailored for monitoring anomalies in network traffic. SpectraTW relies on four spectral indicators, Connectedness, Flooding, Wiriness, and Asymmetry, derived from network attributes and topological variations, that are defined and evaluated. This method interprets networks as evolving graphs, leveraging the Laplacian matrix’s spectral insights to detect shifts in network structure over time. The significance of spectral analysis becomes especially pronounced in the medical IoT domains, where the complex web of devices and the critical nature of healthcare data amplify the need for advanced security measures. Spectral analysis’s ability to swiftly pinpoint irregularities and shift in network traffic aligns well with the medical IoT’s requirements for prompt attack detection.