Scalable Patch Management Using Evolutionary Analysis of Attack Graphs

Network administrators must not only consider the vulnerabilities on each individual machine, but also how those vulnerabilities interact in a networked environment. Attack graphs are a tool to determine these interactions. They allow an administrator to visualize paths an attacker may take to compromise the network. Two critical issues that are often overlooked in analyzing attack graphs are the scalability of the method to large networks and the ability of the administrator to customize the method to the needs of his particular network. This work provides a method based on a multi-objective genetic algorithm to analyze attack graph and determine a minimum set of patches. The method is able to scale to networks containing several hundred machines.