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Cloud infrastructures are being increasingly utilized in criticalinfrastructures such as banking/finance, transportation and utility management.Sophistication and resources used in recent security breaches including thoseon critical infrastructures show that attackers are no longer limited bymonetary/computational constraints. In fact, they may be aided by entities withlarge financial and human resources. Hence there is urgent need to developpredictive approaches for cyber defense to strengthen cloud infrastructuresspecifically utilized by critical infrastructures. Extensive research has beendone in the past on applying techniques such as Game Theory, Machine Learningand Bayesian Networks among others for the predictive defense of criticalinfrastructures. However a major drawback of these approaches is that they donot incorporate probabilistic human behavior which limits their predictiveability. In this paper, a stochastic approach is proposed to predict lesssecure states in critical cloud systems which might lead to potential securitybreaches. These less-secure states are deemed as `risky' states in ourapproach. Markov Decision Process (MDP) is used to accurately incorporate userbehavior(s) as well as operational behavior of the cloud infrastructure througha set of features. The developed reward/cost mechanism is then used to selectappropriate `actions' to identify risky states at future time steps by learningan optimal policy. Experimental results show that the proposed frameworkperforms well in identifying future `risky' states. Through this work wedemonstrate the effectiveness of using probabilistic modeling (MDP) topredictively secure critical cloud infrastructures.

An approach to predictively securing critical cloud infrastructures through probabilistic modeling