Maintaining a resilient computer network is a delicate task with conflicting priorities. Flows should be served while controlling risk due to attackers. Upon publication of a vulnerability, administrators scramble to manually mitigate risk while waiting for a patch. We introduce$\textsc {Fashion}$: a linear optimizer that balances routing flows with the security risk posed by these flows.$\textsc {Fashion}$formalizes routing as a multi-commodity flow problem with side-constraints.$\textsc {Fashion}$formulates security using two approximations of risk in a probabilistic attack graph (Frigault et al. Network Security Metrics 2017).$\textsc {Fashion}$'s output is a set of software-defined networking rules consumable by Frenetic (Foster et al. ICFP 2011). We introduce a topology generation tool that creates data center network instances including flows and vulnerabilities.$\textsc {Fashion}$is executed on instances of up to 600 devices, thousands of flows, and million edge attack graphs. Solve time averages 30 minutes on the largest instances (seconds on the smallest instances). To ensure the security objective is accurate, the output solution is assessed using risk as defined by Frigault et al.$\textsc {Fashion}$allows enterprises to reconfigure their network in response to changes in functionality or security requirements.

Social networks where the actors occupy geospatial locations are prevalent in military, intelligence, and policing operations such as counter-terrorism, counter-insurgency, and combating organized crime. These networks are often derived from a variety of intelligence sources. The discovery of communities that are geographically disperse stems from the requirement to identify higher-level organizational structures, such as a logistics group that provides support to various geographically disperse terrorist cells. We apply a variant of Newman-Girvan modularity to this problem known as distance modularity. To address the problem of finding geographically disperse communities, we modify the well-known Louvain algorithm to find partitions of networks that provide near-optimal solutions to this quantity. We apply this algorithm to numerous samples from two real-world social networks and a terrorism network data set whose nodes have associated geospatial locations. Our experiments show this to be an effective approach and highlight various practical considerations when applying the algorithm to distance modularity maximization. Several military, intelligence, and law-enforcement organizations are working with us to further test and field software for this emerging application.