Towards Network Security Policy Generation for Configuration Analysis and Testing

Access-control lists are an essential part in the security frame-work of any system. Researchers are always in need to have a repository of ready made policies for conducting research and development. Such policies, especially firewall policies which are the focus of our work, are needed to perform per-formance testing as well as configuration analysis. In this paper we introduce a novel technique to perform access-control policy generation. The proposed approach learns policy parameters from a set of given policies. It generates policies that conform with natural policy-writing practices while following the grammar syntax required by the security device. A probabilistic learning approach is used to infer transition probabilities for the policy grammar