Abstract
Protection of sensitive information in platforms such as the ones offered by smart cities requires careful enforcement of access control rules that denote " who can/cannot access to what under which circumstances ". In this paper, we propose our ongoing work on the development of a deep learning technique to infer policies from logs. Our proposal improves the state-of-the-art by supporting negative authorizations (i.e. denied access requests) and different types of noise in logs. A preliminary evaluation of the proposed technique is also presented in the paper.
| Original language | English |
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| Title of host publication | In proc. of the 18th International Multiconference, IS 2015, Intelligent Systems |
| Place of Publication | Ljubljana, Slovenia |
| Publication status | Published - 7 Oct 2015 |
| Event | Intelligent Systems 2015, October 7, 2015, Ljubljana, Slovenia - Ljubljana, Slovenia Duration: 7 Oct 2015 → 7 Oct 2015 |
Conference
| Conference | Intelligent Systems 2015, October 7, 2015, Ljubljana, Slovenia |
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| Country/Territory | Slovenia |
| City | Ljubljana |
| Period | 7/10/15 → 7/10/15 |
| Other | Conference held as a part of the 18th Information Society Multiconference (IS 2015) |
Keywords
- Deep Learning
- Boltzmann Machines
- Density Estimation
- Attribute-Based Access Control
- ABAC Policy Mining
