Name
Abstract | ||
|---|---|---|
We report preliminary studies on evolvable simulations applied to Automated Red Teaming (ART). ART is a vulnerability assessment tool in which agent-based models of simplified military scenarios are repeatedly and automatically generated, executed and varied. Nature-inspired heuristic techniques are utilized to drive the exploration of simulation models to exhibit desired system behaviors. To date, ART investigations have essentially addressed the evolution of a limited fixed set of parameters determining the agents' behavior. We propose to extend ART to widen the range of evolvable simulation model parameters. Using this "evolvable simulation" approach, we conduct experiments in which the agents' structure is evolved. Specifically, a maritime scenario is examined where the individual trajectories of belligerent vessels are evolved to break Blue. These experiments are conducted using a modular evolutionary framework coined CASE. The results present counter-intuitive outcomes and suggest that evolvable simulation is a promising technique to enhance ART. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/WSC.2010.5679047 | Winter Simulation Conference |
Keywords | Field | DocType |
individual trajectory,simulation model,automated red teaming,preliminary study,automated red,evolvable simulation model parameter,art investigation,evolvable simulation,nature-inspired heuristic technique,maritime scenario,belligerent vessel,military scenario,art,computational modeling,computer aided software engineering,evolutionary computation,xml,vulnerability assessment,multi agent systems | Heuristic,XML,Computer science,Simulation,Vulnerability assessment,Evolutionary computation,Multi-agent system,Simulation modeling,Artificial intelligence,Modular design,Computer-aided software engineering | Conference |
ISSN | ISBN | Citations |
0891-7736 | 978-1-4244-9864-2 | 4 |
PageRank | References | Authors |
0.60 | 13 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| James Decraene | 1 | 50 | 10.17 |
| Mahinthan Chandramohan | 2 | 222 | 11.67 |
| Malcolm Yoke Hean Low | 3 | 694 | 52.19 |
| C. S. Choo | 4 | 32 | 3.35 |