Sakuna Harinda Jayasundara, Nalin Asanka Gamagedara Arachchilage, Giovanni Russello (University of Auckland)

Access control failures can cause data breaches, putting entire organizations at risk of financial loss and reputation damage. One of the main reasons for such failures is the mistakes made by system administrators when they manually generate low-level access control policies directly from highlevel requirement specifications. Therefore, to help administrators in that policy generation process, previous research proposed graphical policy authoring tools and automated policy generation frameworks. However, in reality, those tools and frameworks are neither usable nor reliable enough to help administrators generate access control policies accurately while avoiding access control failures. Therefore, as a solution, in this paper, we present “AccessFormer”, a novel policy generation framework that improves both the usability and reliability of access control policy generation. Through the proposed framework, on the one hand, we improve the reliability of policy generation by utilizing Language Models (LMs) to generate, verify, and refine access control policies by incorporating the system’s as well as administrator’s feedback. On the other hand, we also improve the usability of the policy generation by proposing a usable policy authoring interface designed to help administrators understand policy generation mistakes and accurately provide feedback.

View More Papers

EnclaveFuzz: Finding Vulnerabilities in SGX Applications

Liheng Chen (Institute of Information Engineering, Chinese Academy of Sciences; School of Cyber Security, University of Chinese Academy of Sciences; Institute for Network Science and Cyberspace of Tsinghua University), Zheming Li (Institute for Network Science and Cyberspace of Tsinghua University), Zheyu Ma (Institute for Network Science and Cyberspace of Tsinghua University), Yuan Li (Tsinghua University),…

Read More

Exploring the Influence of Prompts in LLMs for Security-Related...

Weiheng Bai (University of Minnesota), Qiushi Wu (IBM Research), Kefu Wu, Kangjie Lu (University of Minnesota)

Read More

REPLICAWATCHER: Training-less Anomaly Detection in Containerized Microservices

Asbat El Khairi (University of Twente), Marco Caselli (Siemens AG), Andreas Peter (University of Oldenburg), Andrea Continella (University of Twente)

Read More

Acoustic Keystroke Leakage on Smart Televisions

Tejas Kannan (University of Chicago), Synthia Qia Wang (University of Chicago), Max Sunog (University of Chicago), Abraham Bueno de Mesquita (University of Chicago Laboratory Schools), Nick Feamster (University of Chicago), Henry Hoffmann (University of Chicago)

Read More