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Analysis Of Key Installation Protection Using Computerized Red Teaming, Tirtha Ranjeet, Philip Hingston, Chiou Peng Lam, Martin Masek
Analysis Of Key Installation Protection Using Computerized Red Teaming, Tirtha Ranjeet, Philip Hingston, Chiou Peng Lam, Martin Masek
Research outputs 2011
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 …
High-Dimensional Objective-Based Data Farming, Zeng Fanchao, James Decraene, Malcolm Low, Wentong Cai, Suiping Zhou, Philip F. Hingston
High-Dimensional Objective-Based Data Farming, Zeng Fanchao, James Decraene, Malcolm Low, Wentong Cai, Suiping Zhou, Philip F. Hingston
Research outputs 2011
In objective-based data farming, decision variables of the Red Team are evolved using evolutionary algorithms such that a series of rigorous Red Team strategies can be generated to assess the Blue Team's operational tactics. Typically, less than 10 decision variables (out of 1000+) are selected by subject matter experts (SMEs) based on their past experience and intuition. While this approach can significantly improve the computing efficiency of the data farming process, it limits the chance of discovering “surprises” and moreover, data farming may be used only to verify SMEs' assumptions. A straightforward solution is simply to evolve all Red Team …