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Towards Adaptive Access Control

  • Luciano Argento
  • Andrea Margheri
  • Federica Paci
  • Vladimiro Sassone
  • Nicola Zannone
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10980)

Abstract

Access control systems are nowadays the first line of defence of modern IT systems. However, their effectiveness is often compromised by policy miscofigurations that can be exploited by insider threats. In this paper, we present an approach based on machine learning to refine attribute-based access control policies in order to reduce the risks of users abusing their privileges. Our approach exploits behavioral patterns representing how users typically access resources to narrow the permissions granted to users when anomalous behaviors are detected. The proposed solution has been implemented and its effectiveness has been experimentally evaluated using a synthetic dataset.

Keywords

Access control Machine learning Policy adaptation Insider threat Runtime monitoring 

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Copyright information

© IFIP International Federation for Information Processing 2018

Authors and Affiliations

  1. 1.University of CalabriaRendeItaly
  2. 2.University of SouthamptonSouthamptonUK
  3. 3.Eindhoven University of TechnologyEindhovenNetherlands

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