Cyber maintenance policy optimization via adaptive learning

摘要

We develop a data-driven adaptive control framework to password management in cyber security systems. A password policy is the frontline of protection against cyber attacks, which contains a set of rules on password length, duration, etc. We assume password has censored lifetime, and policy maker determines the duration of the password without complete knowledge of its true lifetime distribution. We develop a gradient based algorithm integrated with a Bayesian learning framework. We show that our algorithm converges to optimal solution and adapts to non-stationary lifetime data.