CASPR: Context-Aware Security Policy Recommendation

Nowadays, SELinux has been widely used to provide flexible mandatory access control and security policies are critical to maintain the security of operating systems.Strictly speaking, all access requests must be restricted by appropriate policy rules to satisfy the functional requirements of the software or application.However, manually configuring security policy rules is an error-prone and time-consuming task that often requires expert knowledge.Therefore, it is a challenging task to recommend policy rules without anomalies effectively due to the numerous policy rules and the complexity of semantics.The majority of previous research mined information from policies to recommend rules but did not apply to the newly defined types without any rules.In this paper, we propose a contextaware security policy recommendation (CASPR) method that can automatically analyze and refine security policy rules.Contextaware information in CASPR includes policy rules, file locations, audit logs, and attribute information.According to these contextaware information, multiple features are extracted to calculate the similarity of privilege sets.Based on the calculation results, CASPR clusters types by the K-means model and then recommends rules automatically.The method automatically detects anomalies in security policy, namely, constraint conflicts, policy inconsistencies, and permission incompleteness.Further, the detected anomalous policies are refined so that the authorization rules can be effectively enforced.The experiment results confirm the feasibility of the proposed method for recommending effective rules for different versions of policies.We demonstrate the effectiveness of clustering by CASPR and calculate the contribution of each context-aware feature based on SHAP.CASPR not only recommends rules for newly defined types based on context-aware information but also enhances the accuracy of security policy recommendations for existing types, compared to other rule recommendation models.CASPR has an average accuracy of 91.582% and F1-score of 93.761% in recommending rules.Further, three kinds of anomalies in the policies can be detected and automatically repaired.We employ CASPR in multiple operating systems to illustrate the universality.The research has significant implications for security policy recommendation and provides a novel method for policy analysis with great potential.