@article {ElOuazzani2021,
	title = {Proximity Measurement for Hierarchical Categorical Attributes in Big Data},
	journal = {Security and Communication Networks},
	volume = {2021},
	year = {2021},
	note = {cited By 0},
	abstract = {Nearly most of the organizations store massive amounts of data in large databases for research, statistics, and mining purposes. In most cases, much of the accumulated data contain sensitive information belonging to individuals which may breach privacy. Hence, ensuring privacy in big data is considered a very important issue. The concept of privacy aims to protect sensitive information from various attacks that may violate the identity of individuals. Anonymization techniques are considered the best way to ensure privacy in big data. Various works have been already realized, taking into account horizontal clustering. The L-diversity technique is one of those techniques dealing with sensitive numerical and categorical attributes. However, the majority of anonymization techniques using L-diversity principle for hierarchical data cannot resist the similarity attack and therefore cannot ensure privacy carefully. In order to prevent the similarity attack while preserving data utility, a hybrid technique dealing with categorical attributes is proposed in this paper. Furthermore, we highlighted all the steps of our proposed algorithm with detailed comments. Moreover, the algorithm is implemented and evaluated according to a well-known information loss-based criterion which is Normalized Certainty Penalty (NCP). The obtained results show a good balance between privacy and data utility. {\textcopyright} 2021 Zakariae El Ouazzani et al.},
	keywords = {Big data, Categorical attributes, Data utilities, Hierarchical data, Hybrid techniques, Information loss, Large database, Privacy by design, Sensitive informations, Various attacks},
	doi = {10.1155/2021/6612923},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111105073\&doi=10.1155\%2f2021\%2f6612923\&partnerID=40\&md5=a1855aa8b73f4bcdebad1b181b25c7ac},
	author = {El Ouazzani, Z. and Braeken, A. and El Bakkali, H.}
}
@article {Cherkaoui2017206,
	title = {Performance analysis of intrusion detection systems in cloud-based systems},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {10542 LNCS},
	year = {2017},
	note = {cited By 0},
	pages = {206-213},
	abstract = {Cloud computing services are widely used nowadays and need to be more secured for an effective exploitation by the users. One of the most challenging issues in these environments is the security of the hosted data. Many cloud computing providers offer web applications for their clients, this is why the most handling attacks in cloud computing are Distributed Denial of Service (DDoS). In this paper, we provide a comparative performance analysis of intrusion detection systems (IDSs) in a real world lab. The aim is to provide an up to date study for researchers and practitioners to understand the issues related to intrusion detection and to deal with DDoS attacks. This analysis includes intrusion detection rates, time running, etc. In the experiments, we configured a cloud platform using OpenStack and an IDS monitoring the whole network traffic of the web server configured. The results show that Suricata drops fewer packets than Bro and Snort successively when a DDoS attack is happening and detect more malicious packets. {\textcopyright} Springer International Publishing AG 2017.},
	doi = {10.1007/978-3-319-68179-5_18},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85034613510\&doi=10.1007\%2f978-3-319-68179-5_18\&partnerID=40\&md5=3e0ce668710d845db260ee41dabb49b5},
	author = {Cherkaoui, R. and Zbakh, M. and Braeken, A. and Touhafi, A.}
}
