@article {Hammouchi2022,
	title = {STRisk: A Socio-Technical Approach to Assess Hacking Breaches Risk},
	journal = {IEEE Transactions on Dependable and Secure Computing},
	year = {2022},
	note = {cited By 0},
	abstract = {Data breaches have begun to take on new dimensions and their prediction is becoming of great importance to organizations. Prior work has addressed this issue mainly from a technical perspective and neglected other interfering aspects such as the social media dimension. To fill this gap, we propose a STRisk predictive system where we expand the scope of the prediction task by bringing into play the social media dimension. We study over 3800 US organizations including both victim and non-victim organizations. For each organization, we design a profile composed of a variety of externally measured technical indicators and social factors. In addition, to account for unreported incidents, we consider the non-victim sample to be noisy and propose a noise correction approach to correct mislabeled organizations. We then build several machine learning models to predict whether an organization is exposed to experience a hacking breach. By exploiting both technical and social features, we achieve an Area Under Curve (AUC) score exceeding 98\%, which is 12\% higher than the AUC achieved using only technical features. Furthermore, our feature importance analysis reveals that open ports and expired certificates are the best technical predictors, while spreadability and agreeability are the best social predictors. IEEE},
	keywords = {Blogs, Cybe risk assessment, Cybe risk prediction, Data breach, Forecasting, Hacking breach, Learning systems, Malware, Noise measurements, Personal computing, Predictive models, Risk assessment, Risk predictions, Risks assessments, Security, Social networking (online)},
	doi = {10.1109/TDSC.2022.3149208},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124712380\&doi=10.1109\%2fTDSC.2022.3149208\&partnerID=40\&md5=f6b798bf0fc97476d3214d3efbc3597a},
	author = {Hammouchi, H. and Nejjari, N. and Mezzour, G. and Ghogho, M. and Benbrahim, H.}
}
@article {Talei2021,
	title = {Smart building energy inefficiencies detection through time series analysis and unsupervised machine learning},
	journal = {Energies},
	volume = {14},
	number = {19},
	year = {2021},
	note = {cited By 6},
	abstract = {The climate of Houston, classified as a humid subtropical climate with tropical influences, makes the heating, ventilation, and air conditioning (HVAC) systems the largest electricity consumers in buildings. HVAC systems in commercial buildings are usually operated by a centralized control system and/or an energy management system based on a fixed schedule and scheduled control of a zone setpoint, which is not appropriate for many buildings with changing occupancy rates. Lately, as part of energy efficiency analysis, attention has focused on collecting and analyzing smart meters and building-related data, as well as applying supervised learning techniques, to propose new strategies to operate HVAC systems and reduce energy consumption. On the other hand, unsupervised learning techniques have been used to study the consumption information and profile characterization of different buildings after cluster analysis is performed. This paper adopts a different approach by revealing the power of unsupervised learning to cluster data and unveiling hidden patterns. In this study, we also identify energy inefficiencies after exploring the cluster results of a single building{\textquoteright}s HVAC consumption data and building usage data as part of the energy efficiency analysis. Time series analysis and the K-means clustering algorithm are successfully applied to identify new energy-saving opportunities in a highly efficient office building located in the Houston area (TX, USA). The paper uses 1-year data from a highly efficient Leadership in Energy and Environment Design (LEED)-, Energy Star-, and Net Zero-certified building, showing a potential energy savings of 6\% using the K-means algorithm. The results show that clustering is instrumental in helping building managers identify potential additional energy savings. {\textcopyright} 2021 by the authors. Licensee MDPI, Basel, Switzerland.},
	keywords = {Clusterings, Conditioning systems, energy efficiency, Energy management systems, Energy savings, Energy utilization, Energy-savings, Harmonic analysis, Heating ventilation and air conditioning, Houston, HVAC, Internet of things, K-means clustering, Leadership in energy and environment design building, Leadership in energy and environment designs, Learning algorithms, Office buildings, Potential energy, Time series analysis, Time-series analysis, Times series, Tropics, Unsupervised learning},
	doi = {10.3390/en14196042},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115608885\&doi=10.3390\%2fen14196042\&partnerID=40\&md5=e01e465020f683b8a7ccebf61bf08077},
	author = {Talei, H. and Benhaddou, D. and Gamarra, C. and Benbrahim, H. and Essaaidi, M.}
}
@article {Mountassir201373,
	title = {Sentiment classification on arabic corpora: A preliminary cross-study},
	journal = {Document Numerique},
	volume = {16},
	number = {1},
	year = {2013},
	note = {cited By 2},
	pages = {73-96},
	abstract = {The rise of social media (such as online web forums and social networking sites) has attracted interests to mining and analyzing opinions available on the web. The online opinion has become the object of studies in many research areas; especially that called "Opinion Mining and Sentiment Analysis". Several interesting and advanced works were performed on few languages (in particular English). However, there were very few studies on Morphologically Rich Languages such as Arabic. This paper presents the study we have carried out to investigate supervised sentiment classification in an Arabic context. We use two Arabic Corpora which are different in many aspects. We use three common classifiers known by their effectiveness, namely Na{\"\i}ve Bayes, Support Vector Machines and k-Nearest Neighbor. We investigate some settings to identify those that allow achieving the best results. These settings are about stemming type, term frequency thresholding, term weighting and ngram words. We show that Na{\"\i}ve Bayes and Support Vector Machines are competitively effective; however k-Nearest Neighbor{\textquoteright}s effectiveness depends on the corpus. Through this study, we recommend to use light-stemming rather than stemming, to remove terms that occur once, to combine unigram and bigram words and to use presence-based weighting rather than frequency-based one. Our results show also that classification performance can be influenced by documents length, documents homogeneity and the nature of document authors. However, the size of data sets does not have an impact on classification results. {\textcopyright} 2013 Lavoisier.},
	doi = {10.3166/DN.16.1.73-96},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84879022187\&doi=10.3166\%2fDN.16.1.73-96\&partnerID=40\&md5=203c5325261be58119451ab15e8141b5},
	author = {Mountassir, A. and Benbrahim, H. and Berrada, I.}
}
@conference {Mountassir201243,
	title = {Some methods to address the problem of unbalanced sentiment classification in an arabic context},
	booktitle = {CiSt 2012 - Proceedings: 2012 Colloquium in Information Science and Technology},
	year = {2012},
	note = {cited By 2},
	pages = {43-48},
	doi = {10.1109/CIST.2012.6388061},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84872535822\&doi=10.1109\%2fCIST.2012.6388061\&partnerID=40\&md5=81312470e1e0a89c333acbcd558540a2},
	author = {Mountassir, A. and Benbrahim, H. and Berrada, I.}
}
