Risk-Based Access Control Decisions Under Uncertainty

This paper addresses the making of access control decisions under uncertainty, when the benet of doing so outweighs the need to absolutely guarantee these decisions are correct. For instance, when there are limited, costly, or failed communication channels to a policy-decision-point. Previously, local caching of decisions has been proposed, but when a correct decision is not available, either a policy-decision-point must be contacted, or a default decision used. We improve upon this model by using learned classifiers of access control decisions. These classifiers, trained on known decisions, infer decisions when an exact match has not been cached, and uses intuitive notions of utility, damage and uncertainty to determine when an inferred decision is preferred over contacting a remote PDP. Clearly there is uncertainty in the predicted decisions, introducing a degree of risk. Our solution proposes a mechanism to quantify the uncertainty of these decisions and allows administrators to bound the overall risk posture of the system. The learning component continuously refines its models based on inputs from a central policy server in cases where the risk is too high or there is too much uncertainty. We have validated our models by building a prototype system and evaluating it with requests from real access control policies. Our experiments show that over a range of system parameters, it is feasible to use machine learning methods to infer access control policies decisions. Thus our system yields several benefits, including reduced calls to the PDP, reducing latency and communication costs; increased net utility; and increased system survivability.