@conference { ISI:000387917500008,
	title = {Work in Progress: K-Nearest Neighbors Techniques for ABAC Policies Clustering},
	booktitle = {ABAC{\textquoteright}16: PROCEEDINGS OF THE 2016 ACM INTERNATIONAL WORKSHOP ON ATTRIBUTE BASED ACCESS CONTROL},
	year = {2016},
	note = {ACM International Workshop on Attribute Based Access Control (ABAC), New Orleans, LA, MAR 11, 2016},
	pages = {72-75},
	publisher = {Assoc Comp Machinery; ACM SIGSAC},
	organization = {Assoc Comp Machinery; ACM SIGSAC},
	abstract = {In this paper, we present an approach based on the K-Nearest Neighbors algorithms for policies clustering that aims to reduce the ABAC policies dimensionality for high scale systems. Since ABAC considers a very large set of attributes for access decisions, it turns out that using such model for large scale systems might be very complicated. To date, researchers have proposed to use data mining techniques to discover roles for RBAC system construction. In this work in progress, we consider the usage of KNN-based techniques for the classification of ABAC policies based on similarity computations of rules in order to enhance the ABAC flexibility and to reduce the number of policy rules.},
	isbn = {978-1-4503-4079-3},
	doi = {10.1145/2875491.2875497},
	author = {Benkaouz, Yahya and Erradi, Mohammed and Freisleben, Bernd}
}
