Abstract
Red teaming is an approach to studying a task by anticipating the actions of an adversary (“red”) who wishes to affect the achievement (by “blue”) of that task. Computational red teaming is a recent approach that extends in red teaming concept in cyber space and benefits from replacing the physical red and blue with simulated entities. In this study, we focus on the use of multiple strategies in computational red teaming and the factors that influence the selection of strategy. The reason for the use of multiple strategies is to simulate variability observed in human choice. The use of multiple strategies are demonstrated by the generation of diversified solutions by evolutionary robotics while the factors that influence the preferences of strategies are perception and deception. This paper presents an attempt at exploring and modeling the effect of red through the evolutionary method in a synthetic red teaming game environment.
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Abbass, H.A., Alam, S., Bender, A.: Application notes: Mebra: multiobjective evolutionary-based risk assessment. Computational Intelligence Magazine 4, 29–36 (2009), http://dx.doi.org/10.1109/MCI.2009.933098
Alam, S., Shafi, K., Abbass, H.A., Barlow, M.: An ensemble approach for conflict detection in free flight by data mining. Transportation Research Part C 17(3), 298–317 (2009)
Australia, S.: AS/NZ 4360: Risk Management. Standards Australia, Standard, AS/NZS 4360 (1999)
Barreno, M., Nelson, B., Joseph, A.D., Tygar, J.: The security of machine learning. Machine Learning (2010)
Griffith, S.B.: Sun Tzu - The Art of War. Oxford University Press (1963)
Jorgensen, Z., Zhou, Y., Inge, M.: A multiple instance learning strategy for combating good word attacks on spam filters. Journal of Machine Learning Research 9, 1115–1146 (2008)
Lauder, M.: Red dawn: The emergence of a red teaming capability in the canadian forces. Canadian Army Journal 12, 25–36 (2009)
Nelson, B., Joseph, A.D.: Bounding an attack’s complexity for a simple learning model. In: Proceedings of the First Workshop on Tackling Computer System Problems with Machine Learning Techniques (SysML), pp. 1–5 (2006)
Newsome, J., Karp, B., Song, D.: Paragraph: Thwarting signature learning by training maliciously. In: Zamboni, D., Kruegel, C. (eds.) RAID 2006. LNCS, vol. 4219, pp. 81–105. Springer, Heidelberg (2006)
Nolfi, S., Parisi, D., Elman, J.L.: Learning and evolution in neural networks. Adaptive Behavior 3(1), 5–28 (1994), http://adb.sagepub.com/content/3/1/5.abstract
Payne, J.W., Bettman, J.R.: Behavioral decision research: A constructive processing perspective. Annual Review of Psychology 43(1), 87–131 (1992)
Simon, H.A.: A behavioral model of rational choice. The Quarterly Journal of Economics 69(1), 99–118 (1955), http://www.jstor.org/stable/1884852
Veloso, A., Meira Jr., W.: Lazy associative classification for content-based spam detection. In: The Proceedings of the Latin American Web Congress (2006)
Yang, A., Abbass, H.A., Sarker, R.: Characterizing warfare in red teaming. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 36(2), 268–285 (2006)
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Wang, S.L., Shafi, K., Lokan, C., Abbass, H.A. (2013). Neuro-Evolution of Escape Behaviour under High Level of Deception and Noise. In: Kim, JH., Matson, E., Myung, H., Xu, P. (eds) Robot Intelligence Technology and Applications 2012. Advances in Intelligent Systems and Computing, vol 208. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37374-9_64
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DOI: https://doi.org/10.1007/978-3-642-37374-9_64
Publisher Name: Springer, Berlin, Heidelberg
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