@conference {ElHadj20195330,
	title = {Validation and Correction of Large Security Policies: A Clustering and Access Log Based Approach},
	booktitle = {Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018},
	year = {2019},
	note = {cited By 0},
	pages = {5330-5332},
	doi = {10.1109/BigData.2018.8622610},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062588205\&doi=10.1109\%2fBigData.2018.8622610\&partnerID=40\&md5=7c1e7e5b141f9f63ccf2ad00a318664f},
	author = {El Hadj, M.A. and Erradi, M. and Khoumsi, A. and Benkaouz, Y.}
}
@article {ElHadj201786,
	title = {ABAC rule reduction via similarity computation},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {10299 LNCS},
	year = {2017},
	note = {cited By 0},
	pages = {86-100},
	abstract = {Attribute-based access control (ABAC) represents a generic model of access control that provides a high level of flexibility and promotes information and security sharing. Since ABAC considers a large set of attributes for access decisions, using it might get very complicated for large systems. Hence, it is interesting to offer techniques to reduce the number of rules in ABAC policies without affecting the final decision. In this paper, we present an approach based on K-nearest neighbors algorithms for clustering ABAC policies. To the best of our knowledge, it is the first approach that aims to reduce the number of policy rules based on similarity computations. Our evaluation results demonstrate the efficiency of the suggested approach. For instance, the reduction rate can reach up to 10\% for an ABAC policy with more than 9000 rules. {\textcopyright} Springer International Publishing AG 2017.},
	doi = {10.1007/978-3-319-59647-1_7},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019735144\&doi=10.1007\%2f978-3-319-59647-1_7\&partnerID=40\&md5=948de8bee1016664b63163965c4e41fa},
	author = {El Hadj, M.A. and Benkaouz, Y. and Freisleben, B. and Erradi, M.}
}
@conference {ElHadj2017548,
	title = {Clustering-based approach for anomaly detection in XACML policies},
	booktitle = {ICETE 2017 - Proceedings of the 14th International Joint Conference on e-Business and Telecommunications},
	volume = {4},
	year = {2017},
	note = {cited By 0},
	pages = {548-553},
	abstract = {The development of distributed applications arises multiple security issues such as access control. Attribute-Based Access Control has been proposed as a generic access control model, which provides more flexibility and promotes information and security sharing. eXtensible Access Control Markup Language (XACML) is the most convenient way to express ABAC policies. However, in distributed environments, XACML policies become more complex and hard to manage. In fact, an XACML policy in distributed applications may be aggregated from multiple parties and can be managed by more than one administrator. Therefore, it may contain several anomalies such as conflicts and redundancies, which may affect the performance of the policy execution. In this paper, we propose an anomaly detection method based on the decomposition of a policy into clusters before searching anomalies within each cluster. Our evaluation results demonstrate the efficiency of the suggested approach. Copyright {\textcopyright} 2017 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029414026\&partnerID=40\&md5=133521fca6a95f8238cfc3b777dd3534},
	author = {El Hadj, M.A. and Ayache, M. and Benkaouz, Y. and Khoumsi, A. and Erradi, M.}
}
