An approach to predictively securing critical cloud infrastructures
  through probabilistic modeling
  Cloud infrastructures are being increasingly utilized in critical
infrastructures such as banking/finance, transportation and utility management.
Sophistication and resources used in recent security breaches including those
on critical infrastructures show that attackers are no longer limited by
monetary/computational constraints. In fact, they may be aided by entities with
large financial and human resources. Hence there is urgent need to develop
predictive approaches for cyber defense to strengthen cloud infrastructures
specifically utilized by critical infrastructures. Extensive research has been
done in the past on applying techniques such as Game Theory, Machine Learning
and Bayesian Networks among others for the predictive defense of critical
infrastructures. However a major drawback of these approaches is that they do
not incorporate probabilistic human behavior which limits their predictive
ability. In this paper, a stochastic approach is proposed to predict less
secure states in critical cloud systems which might lead to potential security
breaches. These less-secure states are deemed as `risky' states in our
approach. Markov Decision Process (MDP) is used to accurately incorporate user
behavior(s) as well as operational behavior of the cloud infrastructure through
a set of features. The developed reward/cost mechanism is then used to select
appropriate `actions' to identify risky states at future time steps by learning
an optimal policy. Experimental results show that the proposed framework
performs well in identifying future `risky' states. Through this work we
demonstrate the effectiveness of using probabilistic modeling (MDP) to
predictively secure critical cloud infrastructures.
