Automated design of multi-level network partitioning heuristics employing self-adaptive primitive granularity control

Network segmentation has a variety of applications, including computer network security. A well segmented computer network is less likely to result in information leaks and more resilient to adversarial traversal. Conventionally network segmentation approaches rely on graph partitioning algorithms. However, general-purpose graph partitioning solutions are just that, general purpose. These approaches do not exploit specific topological characteristics present in certain classes of networks. Tailored partition methods can be developed to target specific domains, but this process can be time consuming and difficult. This work builds on previous research employing generative hyper-heuristics in the form of genetic programming for automating the development of customized graph partitioning heuristics by incorporating a dynamic approach to controlling the granularity of the heuristic search. The potential of this approach is demonstrated using two real-world complex network applications. Results show that the automated design process is capable of fine tuning graph partitioning heuristics that sacrifice generality for improved performance on targeted networks.