A Distributed and Self-Organizing Attack Model in Hyperbolically Embedded Networks

Hyperbolically embedded networks have emerged as powerful structures for efficient routing and management in diverse fields, including internet infrastructure, edge computing, and IoT environments. Leveraging the rich geometric information inherent in hyperbolic coordinates-such as hierarchy, popularity, and node similarity-these networks achieve scalability and optimal performance. However, the detailed structural information encoded by these coordinates also exposes such networks to novel vulnerabilities. This study introduces a distributed, selforganizing attack model specifically designed to exploit hyperbolic embeddings through strategic link removals. Nodes independently identify inter-sector connections based solely on local geometric coordinates, progressively dismantling the network's global connectivity. Our comparative evaluation demonstrates that the proposed sector-based attack efficiently fragments the network while removing fewer links compared to traditional partitioning methods. Analysis of edge removal distribution reveals insights into node vulnerability patterns and network resilience. This work highlights the necessity of addressing such security threats proactively and discusses potential countermeasures, underscoring the inherent challenges due to the global nature of geometric embeddings.