This paper presents an agent-based model to analyse behaviour produced under noisy and deceptive information conditions. A simple yet powerful simulation environment is developed where adaptive agents act and adapt to varying levels of information quality that they sense about their environment. The simulation environment consists of two types of agents moving in a bounded two-dimensional continuous plane: a neuro-evolutionary learning agent that adapts its manoeuvreing strategies to escape a pre-programmed deceptive agent; and a pre-programmed agent, whose goal is to capture the adaptive agent, that acts on noisy information about the adaptive agent’s manoeuvres that it senses from the environment. The pre-programmed agent is also able to produce deceptive actions to confuse the adaptive agent. The behaviour is represented in terms of the manoeuvreing strategies that the agents adopt as their actions to the environmental changes. A behaviour analysis methodology is developed to compare agent actions under different information conditions, that elicits interesting relationships between behaviour and the studied information conditions. The framework is easily extendable to analyse human behaviour in similar environments by replacing the adaptive agent with an interactive human–machine interface.
AbbassH. A. (2009). Computational red teaming and cyber challenges. Platform Technologies Research Institute Annual Symposium, PTRI 09, 14–15 July 2009. (Invited Speech.)
2.
AbbassH. A.BenderA.GaidowS.WhitbreadP. (2011). Computational red teaming: Past, present and future. IEEE Computational Intelligence Magazine, 6(1), 30–42.
3.
AbbassH. A.BenderA.WhitbreadP. (2010a). Computational red teaming. In Defence operations research symposium (DORS). Adelaide, Australia.
4.
AbbassH. A.BenderA.WhitbreadP. (2010b). Computational red teaming: Unravelling the pharaoh’s curse. In Computational Intelligence in Security and Defence Applications Workshop. Barcelona, Spain.
5.
BarghJ.ChenM.BurrowsL. (1996). Automaticity of social behavior: Direct effects of trait construct and stereotype activation on action. Journal of Personality and Social Psychology, 71(2), 230.
6.
BarrenoM.BartlettP. L.ChiF. J.JosephA. D.NelsonB.RubinsteinB. I. P.… TygarJ. D. (2008). Open problems in the security of learning. In Proceedings of the 1st ACM workshop on AISec (pp. 19–26). New York, NY: ACM.
7.
BarrenoM.NelsonB.JosephA. D.TygarJ. D. (2010). The security of machine learning. Machine Learning, 81(2), 121–148.
8.
BarrenoM.NelsonB.SearsR.JosephA. D.TygarJ. D. (2006). Can machine learning be secure? In Proceedings of the 2006 ACM symposium on information, computer and communications security (ASIACCS) (pp. 16–25). New York, NY: ACM Press.
BezdekJ. C. (1981). Pattern recognition with fuzzy objective function algorithms. Norwell, MA: Kluwer.
11.
CaliáskiT.HarabaszJ. (1974). A dendrite method for cluster analysis. Communications in Statistics – Theory and Methods, 3(1), 1–27.
12.
ChaturvediA.GuptaM.MehtaS.YueW. (2000). Agent-based simulation approach to information warfare in the SEAS environment. In Proceedings of the 33rd annual Hawaii international conference on system sciences (pp. 1–10).
13.
DalviN.DomingosP.MausamSanghaiS.VermaD. (2004). Adversarial classification. In Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining (pp. 99–108). New York, NY: ACM.
14.
DaviesD. L.BouldinD. W. (1979). A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-1(2), 224–227.
15.
DunnJ. C. (1974). Well-separated clusters and optimal fuzzy partitions. Journal of Cybernetics, 4(1), 95–104.
16.
EinhornH. J.HogarthR. M. (1981). Behavioral decision theory: Processes of judgement and choice. Annual Review of Psychology, 32(1), 53–88.
17.
FloreanoD.MondadaF. (1994). Automatic creation of an autonomous agent: Genetic evolution of a neural-network driven robot. From Animals to Animats, 3, 421–430.
18.
GomezF.MiikkulainenR. (1997). Incremental evolution of complex general behavior. Adaptive Behavior, 5(3–4), 317–342.
19.
HarveyI.Di PaoloE.WoodR.QuinnM.TuciE.IridiaE. (2005). Evolutionary robotics: A new scientific tool for studying cognition. Artificial Life, 11(1–2), 79–98.
20.
JorgensenZ.ZhouY.IngeM. (2008). A multiple instance learning strategy for combating good word attacks on spam filters. Journal of Machine Learning Research, 9, 1115–1146.
21.
LauderM. (2009). Red dawn: The emergence of a red teaming capability in the Canadian forces. Canadian Army Journal, 12(2), 25–36.
22.
LimaS. (2002). Putting predators back into behavioral predator–prey interactions. Trends in Ecology & Evolution, 17(2), 70–75.
23.
LowdD.MeekC. (2005). Adversarial learning. In Proceedings of the eleventh ACM SIGKDD international conference on knowledge discovery in data mining (pp. 641–647). New York, NY: ACM.
24.
MaesP. (1990). Situated agents can have goals. Robotics and Autonomous Systems, 6(1–2), 49–70.
25.
MaulikU.BandyopadhyayS. (2002). Performance evaluation of clustering algorithms and validity indices. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(12), 1650–1654.
26.
NelsonB.BarrenoM.Jack ChiF.JosephA. D.RubinsteinB. I. P.SainiU.… XiaK. (2009). Misleading a learner: Coopting your spam filter. In Machine learning in cyber trust (pp. 17–51). New York, NY: Springer.
27.
NelsonB.JosephA. D. (2006). 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).
28.
NewsomeJ.KarpB.SongD. (2006). Paragraph: Thwarting signature learning by training maliciously. In Recent advances in intrusion detection (pp. 81–105). Berlin/Heidelberg, Germany: Springer.
29.
NolfiS.FloreanoD. (2000). Evolutionary robotics: The biology, intelligence, and technology of self-organizing machines. Cambridge, MA: The MIT Press.
30.
NolfiS.ParisiD.ElmanJ. L. (1994). Learning and evolution in neural networks. Adaptive Behavior, 3(1), 5–28.
31.
RileyP.VelosoM. (2000). On behavior classification in adversarial environments. Distributed autonomous robotic systems, 4, 371–380.
32.
RousseeuwP. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53–65.
33.
SimonH. A. (1955). A behavioral model of rational choice. The Quarterly Journal of Economics, 69(1), 99–118.
34.
VallacherR.WegnerD. (1987). What do people think they’re doing? Action identification and human behavior. Psychological Review, 94(1), 3.
35.
VelosoA.MeiraW.Jr. (2006). Lazy associative classification for content-based spam detection. In Proceedings of the fourth Latin American web congress (pp. 154–161).
36.
VendraminL.CampelloR. J. G. B.HruschkaE. R. (2009). On the comparison of relative clustering validity criteria. In Proceedings of the Siam international conference on data mining (pp. 733–744). Sparks, NV.
37.
YangA.AbbassH. A.SarkerR. (2006). Characterizing warfare in red teaming. IEEE Transactions on Systems, Man, and Cybernetics – Part B: Cybernetics, 36(2), 268–285.
