Towards ABAC Policy Mining from Logs with Deep Learning

Decebal Mocanu, Fatih Turkmen, Antonio Liotta

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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 languageEnglish
Title of host publicationIn proc. of the 18th International Multiconference, IS 2015, Intelligent Systems
Place of PublicationLjubljana, Slovenia
Publication statusPublished - 7 Oct 2015
EventIntelligent Systems 2015, October 7, 2015, Ljubljana, Slovenia - Ljubljana, Slovenia
Duration: 7 Oct 20157 Oct 2015

Conference

ConferenceIntelligent Systems 2015, October 7, 2015, Ljubljana, Slovenia
CountrySlovenia
CityLjubljana
Period7/10/157/10/15
OtherConference 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

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