Enhancing Cyber Resilience Through Traffic Generation Patterns in Complex Networks: A Study on Cascading Failures

Network resilience is the capacity of a network to maintain and restore its fundamental operations during or after a failure. This paper investigates the resilience of communication networks with heterogeneous nodes, with host nodes that generate and receive packets and routers that only forward packets. We focus on how traffic generation patterns, defined as the distribution of data packet creation across hosts, affect network resilience. While previous studies identified optimal host placements that balance traffic loads and enhance network performance, this research explores how traffic generation patterns influence network resilience, particularly during cascading failures, where the failure of one node triggers subsequent failures across the network. To address this issue, we model our networks as unweighted, undirected graphs consisting of non-mobile nodes and use the host survival rate—the percentage of functional hosts after a failure—as the network’s critical function. Assuming the network includes a host-based restoration process as an inherent recovery mechanism that initiates after an incident, we compare the resilience of different network topologies (scale-free, regular lattice, and Erdőos-Rényi), by conducting computational experiments to assess the effect of various traffic generation patterns by placing hosts on high-degree, low-degree, or randomly selected nodes. This is done by developing a discrete mathematical framework for measuring cyber resilience as an integral of the critical function in discrete time steps. Our findings indicate that when the hosts have multiple connections to other nodes, it significantly enhances network resilience. Notably, scale-free networks demonstrate more than twice the resilience compared to other topologies, showing a host survival rate improvement from 0.2093 to 0.4498 when hosts are positioned on high-degree nodes as opposed to being placed randomly.