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Obtaining an accurate specification of the access control policy enforced by an application is essential in ensuring that it meets our security/privacy expectations. This is especially important as many of real-world applications handle a large amount and variety of data objects that may have different applicable policies. We investigate the problem of automated learning of access control policies from web applications. The existing research on mining access control policies has mainly focused on developing algorithms for inferring correct and concise policies from low-level authorization information. However, little has been done in terms of systematically gathering the low-level authorization data and applications' data models that are prerequisite to such a mining process. In this paper, we propose a novel black-box approach to inferring those prerequisites and discuss our initial observations on employing such a framework in learning policies from real-world web applications.
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Mining Shape Expressions from Positive Examples
Shape expressions (SEs) is a novel specification language that was recently introduced to express behavioral patterns over real-valued signals observed during the execution of cyber-physical systems. An SE is a regular expression composed of arbitrary parameterized shapes, such as lines, exponential curves, and sinusoids as atomic symbols with symbolic constraints on the shape parameters. SEs enable a natural and intuitive specification of complex temporal patterns over possibly noisy data. In this article, we propose a novel method for mining a broad and interesting fragment of SEs from time-series data using a combination of techniques from linear regression, unsupervised clustering, and learning finite automata from positive examples. The learned SE for a given dataset provides an explainable and intuitive model of the observed system behavior. We demonstrate the applicability of our approach on two case studies from different application domains and experimentally evaluate the implemented specification mining procedure.
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- Award ID(s):
- 1837131
- PAR ID:
- 10199915
- Date Published:
- Journal Name:
- IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
- ISSN:
- 0278-0070
- Page Range / eLocation ID:
- 1 to 1
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/TCAD.2020.3012240
