D. Agarwal, An Empirical Bayes Approach to Detect Anomalies in Dynamic Multidimensional Arrays, Fifth IEEE International Conference on Data Mining (ICDM'05), 2005.
DOI : 10.1109/ICDM.2005.22

F. Angiulli, S. Basta, and C. Pizzuti, Distance-based detection and prediction of outliers, IEEE transactions on knowledge and data engineering, 2006.
DOI : 10.1109/tkde.2006.29

F. Angiulli and F. Fassetti, Detecting distance-based outliers in streams of data, Proceedings of the sixteenth ACM conference on Conference on information and knowledge management , CIKM '07, 2007.
DOI : 10.1145/1321440.1321552

A. A. Balkema and L. De-haan, Residual life time at great age. The Annals of probability, 1974.
DOI : 10.1214/aop/1176996548
URL : http://doi.org/10.1214/aop/1176996548

J. Beirlant, Y. Goegebeur, J. Segers, and J. Teugels, Statistics of extremes: theory and applications, 2006.
DOI : 10.1002/0470012382

R. H. Byrd, P. Lu, J. Nocedal, and C. Zhu, A limited memory algorithm for bound constrained optimization, SIAM J. on Scientific Computing, 1995.
DOI : 10.2172/204262

V. Chandola, A. Banerjee, V. Kumar, L. Duan, L. Xu et al., Anomaly detection: A survey ACM computing surveys Cluster-based outlier detection, Annals of Operations Research, vol.168, issue.1, pp.151-168, 2009.

M. Elahi, K. Li, W. Nisar, X. Lv, and H. Wang, Efficient Clustering-Based Outlier Detection Algorithm for Dynamic Data Stream, 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery, 2008.
DOI : 10.1109/FSKD.2008.374

E. Eskin, Anomaly detection over noisy data using learned probability distributions, ICML, 2000.

G. Fernandes and P. Owezarski, Automated Classification of Network Traffic Anomalies, ICSPCS, 2009.
DOI : 10.1109/TDSC.2007.12

R. A. Fisher and L. H. Tippett, Limiting forms of the frequency distribution of the largest or smallest member of a sample, Mathematical Proceedings of the Cambridge Philosophical Society, 1928.
DOI : 10.1017/S0305004100015681

R. Fontugne, P. Borgnat, P. Abry, and K. Fukuda, MAWILab, Proceedings of the 6th International COnference on, Co-NEXT '10, 2010.
DOI : 10.1145/1921168.1921179
URL : https://hal.archives-ouvertes.fr/ensl-00552071

B. Gnedenko, Sur La Distribution Limite Du Terme Maximum D'Une Serie Aleatoire, The Annals of Mathematics, vol.44, issue.3, pp.423-453, 1943.
DOI : 10.2307/1968974

S. D. Grimshaw, Computing Maximum Likelihood Estimates for the Generalized Pareto Distribution, Technometrics, vol.4, issue.2, pp.185-191, 1993.
DOI : 10.1214/ss/1177012400

B. M. Hill, A simple general approach to inference about the tail of a distribution. The annals of statistics, pp.1163-1174, 1975.

L. I. Kuncheva, Classifier ensembles for detecting concept change in streaming data: Overview and perspectives, 2nd Workshop SUEMA, 2008.

R. Laxhammar and G. Falkman, Online Learning and Sequential Anomaly Detection in Trajectories, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.36, issue.6, pp.1158-1173, 2014.
DOI : 10.1109/TPAMI.2013.172

J. Mazel, R. Fontugne, and K. Fukuda, A taxonomy of anomalies in backbone network traffic, 2014 International Wireless Communications and Mobile Computing Conference (IWCMC), 2014.
DOI : 10.1109/IWCMC.2014.6906328

E. Ngai, Y. Hu, Y. Wong, Y. Chen, and X. Sun, The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature, Decision Support Systems, vol.50, issue.3, pp.559-569, 2011.
DOI : 10.1016/j.dss.2010.08.006

M. S. Sadik and L. Gruenwald, DBOD-DS: Distance Based Outlier Detection for Data Streams, International Conference on Database and Expert Systems Applications, pp.122-136, 2010.
DOI : 10.1007/978-3-642-15364-8_9

S. Sadik and L. Gruenwald, Research issues in outlier detection for data streams, ACM SIGKDD Explorations Newsletter, vol.15, issue.1, pp.33-40, 2014.
DOI : 10.1145/2594473.2594479
URL : http://www.sigkdd.org/sites/default/files/issues/15-1-2013-06/V15-01-04-Sadik.pdf

J. E. Seem, Using intelligent data analysis to detect abnormal energy consumption in buildings. Energy and buildings, pp.52-58, 2007.

H. Wang, D. Zhang, and K. G. Shin, Detecting syn flooding attacks, INFOCOM, 2002.