Resilient Architecture of All-Optical Networks: Probabilistic Graphical Models for Crosstalk Attack Propagation

We study the resilience of all-optical network (AON) architectures under in-band crosstalk attacks. We first develop a cross-layer model that captures attack propagation based on probabilistic graphical models. At the physical layer, we use a directed probabilistic graph (Bayesian belief network) to model the attack propagation under static network traffic and a given source of attack. At the network layer, we use an undirected probabilistic graph (random field) to represent the probability distribution of active connections in the network. The cross-layer model is obtained by combining the physicaland the network-layer models into a factor graph representation. We then derive bounds on the network resilience for regular topologies. We show that for ring, star, and mesh-torus networks with link-shortest path routing and all-to-all traffic, the average network resilience loss grows linearly with respect to the network load when the network load is light; grows polynomially with respect to the probability of attack propagation from node to node along the attacker´s route. We then show that the sum-product algorithm can be used for computationally efficient evaluation of network resilience for irregular topologies