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Conference Paper
Lugo, Anthony Erb, Garcia, Dennis, Hemberg, Erik, O'Reilly, Una-May.  2017.  Developing Proactive Defenses for Computer Networks with Coevolutionary Genetic Algorithms. Proceedings of the Genetic and Evolutionary Computation Conference Companion. :273–274.
Our cybersecurity tool, RIVALS, develops adaptive network defense strategies by modeling adversarial network attack and defense behavior in peer-to-peer networks via coevolutionary algorithms. Currently RIVALS DOS attacks are modestly modeled by the selection of a node that is completely disabled for a resource-limited duration. Defenders have three different network routing protocols. Attack or mission completion and resource cost metrics serve as attacker and defender objectives. This work also includes a description of RIVALS' suite of coevolutionary algorithms that explore archiving as a means of maintaining progressive exploration and support the evaluation of different solution concepts. To compare and contrast the effectiveness of each algorithm, we execute simulations on 3 different network topologies. Our experiments show that it is possible to forgo the assurance of monotonically increasing results and still retain high quality results.
Picek, Stjepan, Hemberg, Erik, O'Reilly, Una-May.  2017.  If You Can'T Measure It, You Can'T Improve It: Moving Target Defense Metrics. Proceedings of the 2017 Workshop on Moving Target Defense. :115–118.
We propose new metrics drawing inspiration from the optimization domain that can be used to characterize the effectiveness of moving target defenses better. Besides that, we propose a Network Neighborhood Partitioning algorithm that can help to measure the influence of MTDs more precisely. The techniques proposed here are generic and could be combined with existing metrics. The obtained results demonstrate how additional information about the effectiveness of defenses can be obtained as well as how network neighborhood partitioning helps to improve the granularity of metrics.
Picek, Stjepan, Hemberg, Erik, O'Reilly, Una-May.  2017.  If You Can'T Measure It, You Can'T Improve It: Moving Target Defense Metrics. Proceedings of the 2017 Workshop on Moving Target Defense. :115–118.
We propose new metrics drawing inspiration from the optimization domain that can be used to characterize the effectiveness of moving target defenses better. Besides that, we propose a Network Neighborhood Partitioning algorithm that can help to measure the influence of MTDs more precisely. The techniques proposed here are generic and could be combined with existing metrics. The obtained results demonstrate how additional information about the effectiveness of defenses can be obtained as well as how network neighborhood partitioning helps to improve the granularity of metrics.
Garcia, Dennis, Lugo, Anthony Erb, Hemberg, Erik, O'Reilly, Una-May.  2017.  Investigating Coevolutionary Archive Based Genetic Algorithms on Cyber Defense Networks. Proceedings of the Genetic and Evolutionary Computation Conference Companion. :1455–1462.
We introduce a new cybersecurity project named RIVALS. RIVALS will assist in developing network defense strategies through modeling adversarial network attack and defense dynamics. RIVALS will focus on peer-to-peer networks and use coevolutionary algorithms. In this contribution, we describe RIVALS' current suite of coevolutionary algorithms that use archiving to maintain progressive exploration and that support different solution concepts as fitness metrics. We compare and contrast their effectiveness by executing a standard coevolutionary benchmark (Compare-on-one) and RIVALS simulations on 3 different network topologies. Currently, we model denial of service (DOS) attack strategies by the attacker selecting one or more network servers to disable for some duration. Defenders can choose one of three different network routing protocols: shortest path, flooding and a peer-to-peer ring overlay to try to maintain their performance. Attack completion and resource cost minimization serve as attacker objectives. Mission completion and resource cost minimization are the reciprocal defender objectives. Our experiments show that existing algorithms either sacrifice execution speed or forgo the assurance of consistent results. rIPCA, our adaptation of a known coevolutionary algorithm named IPC A, is able to more consistently produce high quality results, albeit without IPCA's guarantees for results with monotonically increasing performance, without sacrificing speed.