Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multi-objective genetic algorithms: Problem difficulties and construction of test problems
Evolutionary Computation
Automated red teaming: an objective-based data farming approach for red teaming
Proceedings of the 40th Conference on Winter Simulation
Enhancing automated red teaming with evolvable simulation
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Characterizing warfare in red teaming
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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This paper describes the use of genetic algorithms (GAs) for computerized red teaming applications, to explore options for military plans in specific scenarios. A tool called Optimized Red Teaming (ORT) is developed and we illustrate how it may be utilized to assist the red teaming process in security organizations, such as military forces. The developed technique incorporates a genetic algorithm in conjunction with an agent-based simulation system (ABS) called MANA (Map Aware Non-uniform Automata). Both enemy forces (the red team) and friendly forces (the blue team) are modelled as intelligent agents in a multi-agent system and many computer simulations of a scenario are run, pitting the red team plan against the blue team plan. The paper contains two major sections. First, we present a description of the ORT tool, including its various components. Second, experimental results obtained using ORT on a specific military scenario known as Key Installation Protection, developed at DSO National Laboratories in Singapore, are presented. The aim of these experiments is to explore the red tactics to penetrate a fixed blue patrolling strategy.