@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.}
}
@conference {Touhafi20181,
	title = {CloudLab-weaver: An innovative approach to interconnect distributed cloud based online labs},
	booktitle = {Proceedings of 2017 International Conference of Cloud Computing Technologies and Applications, CloudTech 2017},
	volume = {2018-January},
	year = {2018},
	pages = {1-7},
	doi = {10.1109/CloudTech.2017.8284739},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046685180\&doi=10.1109\%2fCloudTech.2017.8284739\&partnerID=40\&md5=f23d16e0a04a343a36ac33365447f357},
	author = {Touhafi, A. and Braeken, A. and Alla, S.B. and Zbakh, M.}
}
@article {Touhafi2018,
	title = {CoderLabs: A cloud-based platform for real-time online labs with user collaboration},
	journal = {Concurrency Computation},
	year = {2018},
	note = {cited By 0; Article in Press},
	abstract = {In this paper, we describe the architecture of a collaborative and real-time environment for remote experiments. We also introduce a web-based remote lab composer, which allows the interconnection and exchange of data between remote laboratories. The environment is created with web standards as HTML5 such that no plug-in needs to be installed by the user. Users are able to use the remote lab simultaneously and in collaboration. This collaboration between users is made feasible by adopting a message broker. We will discuss how Google Coder is used to develop, change, or create a user interface for a remote experiment and how the labs can be shared in the cloud. Our newest addition to this research consists of a drag and drop lab composer, which allows Lab-developers to use standard widgets, data-visualization tools, and data-ports to compose complex remote labs. A lab-composer engine is developed to automate the coupling of the physical instances and collect the data to be visualized. The principles behind our lab-composer engine are explained in the last part of the paper. {\textcopyright} 2018 John Wiley \& Sons, Ltd.},
	doi = {10.1002/cpe.4377},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040200242\&doi=10.1002\%2fcpe.4377\&partnerID=40\&md5=87ce9e075bf09d4169586e7b239f683e},
	author = {Touhafi, A. and Braeken, A. and Tahiri, A. and Zbakh, M.}
}
@article {Touhafi2018,
	title = {Coderlabs: A cloud-based platform for real-time online labs with user collaboration},
	journal = {Concurrency Computation},
	volume = {30},
	number = {12},
	year = {2018},
	doi = {10.1002/cpe.4377},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040200242\&doi=10.1002\%2fcpe.4377\&partnerID=40\&md5=87ce9e075bf09d4169586e7b239f683e},
	author = {Touhafi, A. and Braeken, A. and Tahiri, A. and Zbakh, M.}
}
@conference {Touhafi2017317,
	title = {CoderLabs: A cloud based platform for real time online labs with user collaboration},
	booktitle = {Proceedings of 2016 International Conference on Cloud Computing Technologies and Applications, CloudTech 2016},
	year = {2017},
	note = {cited By 0},
	pages = {317-324},
	abstract = {In this paper we describe the architecture of a real time environment for numerous remote experiments. The environment is created with web standards as HTML5 such that no plug-in needs to be installed by the user. Users are able to use the remote lab simultaneously and in collaboration. This collaboration between users is made feasible by adopting a message broker. Finally, by using Google Coder, developers can easily change or create the user interface of their remote experiments and share experiments in the cloud. {\textcopyright} 2016 IEEE.},
	doi = {10.1109/CloudTech.2016.7847716},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85013760325\&doi=10.1109\%2fCloudTech.2016.7847716\&partnerID=40\&md5=a6e262cd949bd66450aa627b3aa53c03},
	author = {Touhafi, A. and Braeken, A. and Tahiri, A. and Zbakh, M.}
}
@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.}
}
