Understanding how to implement file system access control rules within a system is heavily reliant on expert knowledge, both that intrinsic to how a system can be configured as well as how a current configuration is structured. Maintaining the required level of expertise in fast-changing environments, where frequent configuration changes are implemented, can be challenging. Another set of complexities lies in gaining structural understanding of large volumes of permission information. The accuracy of a new addition within a file system access control is essential, as inadvertently assigning rights that result in a higher than necessary level of access can generate unintended vulnerabilities. To address these issues, a novel mechanism is devised to automatically process a system’s event history to determine how previous access control configuration actions have been implemented and then utilise the model for suggesting how to implement new access control rules. Throughout this paper, we focus on Microsoft’s New Technology File System permissions (NTFS) access control through processing operating system generated log data. We demonstrate how the novel technique can be utilised to plan for the administrator when assigning new permissions. The plans are then evaluated in terms of their validity as well as the reduction in required expert knowledge.
|Title of host publication||Guide to Vulnerability Analysis for Computer Networks and Systems|
|Subtitle of host publication||An Artificial Intelligence Approach|
|Editors||Simon Parkinson, Andrew Crampton, Richard Hill|
|Publication status||Published - 5 Sep 2018|
|Name||Computer Communications and Networks|
Khan, S., & Parkinson, S. (2018). Automated Planning of Administrative Tasks Using Historic Events: A File System Case Study. In S. Parkinson, A. Crampton, & R. Hill (Eds.), Guide to Vulnerability Analysis for Computer Networks and Systems: An Artificial Intelligence Approach (pp. 159-182). (Computer Communications and Networks). Springer, Cham. https://doi.org/10.1007/978-3-319-92624-7_7