, Convention Industrielle de Formation par la Recherche" (CIFRE) proposée par l'Agence Nationale de la Recherche Technique (ANRT) Ce mode de financement consiste en un partenariat entre une entreprise et une université Dans le cas de la présente thèse CIFRE, les partenaires ont été d'une part
, lizeo-online-media-group.com Plan du chapitre 4.1 Introduction, p.75
, 76 4.3 Reconstruction des observations d'une série temporelle, p.77
, , p.78
, , p.79
,
,
,
, , p.86
, , p.86
, 88 A Liste des Publications Revue internationale ? Seif-Eddine Benkabou, Khalid Benabdeslem & Canitia Bruno. Unsupervised Outlier Detection for Time Series by Entropy and Dynamic Time Warping, Knowledge and Information Systems, vol.54, issue.2, pp.463-486, 2018.
, Local-to-Global Unsupervised Anomaly Detection from Temporal Data, Advances in Knowledge Discovery and Data Mining -21st Pacific-Asia Conference, PAKDD 2017 Proceedings, Part I, pp.762-772, 2017.
, L2-type regularization based unsupervised anomaly detection from temporal data, 2017 International Joint Conference on Neural Networks, pp.2354-2361, 2017.
, Outlier Detection for High Dimensional Data, Proceedings of the 2001 ACM SIGMOD international conference on Management of data, pp.37-46, 2001.
, On Abnormality Detection in Spuriously Populated Data Streams, Proceedings of the 2005 SIAM International Conference on Data Mining, SDM 2005, pp.80-91, 2005.
, Outlier Detection with Uncertain Data, Proceedings of the SIAM International Conference on Data Mining, SDM 2008, pp.483-493, 2008.
, Outlier detection in graph streams, Proceedings of the 27th International Conference on Data Engineering, ICDE 2011, pp.399-409, 2011.
, Event Detection in Social Streams, Proceedings of the Twelfth SIAM International Conference on Data Mining, pp.624-635, 2012.
, Outlier analysis, 2007.
, Detecting Distance-based Outliers in Streams of Data, Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management , CIKM '07, pp.811-820, 2007.
, Similarity Search on Time Series Based on Threshold Queries, EDBT, pp.276-294, 2006.
DOI : 10.1007/11687238_19
, A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains, The Annals of Mathematical Statistics, vol.41, issue.1, pp.164-171, 1970.
DOI : 10.1214/aoms/1177697196
, Une approche à deux niveaux séquentiels pour la détection de nouveautés à partir de séries temporelles, 22ème Rencontres de la Société Francophone de Classification, Société Francophone de Classification, pp.129-132, 2015.
, Entropy-based clustering for anomaly detection from time-series data, ICML Workshop on Anomaly detection, p.39, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01340978
, Une approche embedded pour la détection de nouveautés à partir de séries temporelles, Société Francophone de Classification (SFC), 2016.
, Régularisation Ridge pour la détection non-supervisée à partir de séries temporelles, SFC : Société Francophone de Classification, 2017.
, L2-type regularization-based unsupervised anomaly detection from temporal data, 2017 International Joint Conference on Neural Networks, pp.2354-2361, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01458412
, Local-to-Global Unsupervised Anomaly Detection from Temporal Data, Advances in Knowledge Discovery and Data Mining -21st Pacific-Asia Conference, PAKDD 2017 Proceedings, Part I, pp.762-772, 2017.
DOI : 10.1007/BF01074755
URL : https://hal.archives-ouvertes.fr/hal-01458411
, Unsupervised outlier detection for time series by entropy and dynamic time warping, Knowledge and Information Systems, vol.4, issue.1, pp.463-486, 2018.
DOI : 10.1007/BF01074755
URL : https://hal.archives-ouvertes.fr/hal-01528252
, Using Dynamic Time Warping to Find Patterns in Time Series, Knowledge Discovery in Databases : Papers from the 1994 AAAI Workshop, pp.359-370, 1994.
, Spatio-temporal outlier detection in large databases, 28th International Conference on Information Technology Interfaces, pp.179-184, 2006.
, LOF : Identifying Density-based Local Outliers, Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, SIGMOD '00, pp.93-104, 2000.
, Discords in Time Series Database, SDM, pp.449-454, 2007.
DOI : 10.1137/1.9781611972771.43
, Anomaly Detection in Large Sets of High-Dimensional Symbol Sequences, p.23, 2006.
, Anomaly Detection and Diagnosis Algorithms for Discrete Symbol Sequences with Applications to Airline Safety, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol.39, issue.1, pp.101-113, 2009.
DOI : 10.1109/TSMCC.2008.2007248
, Detection and Classification of Intrusions and Faults Using Sequences of System Calls, SIGMOD Rec, vol.30, issue.4, pp.25-34, 2001.
, Comparative Evaluation of Anomaly Detection Techniques for Sequence Data, 2008 Eighth IEEE International Conference on Data Mining, pp.743-748, 2008.
DOI : 10.1109/ICDM.2008.151
, Anomaly detection, ACM Computing Surveys, vol.41, issue.3, pp.1-15, 2009.
DOI : 10.1145/1541880.1541882
, Anomaly Detection for Discrete Sequences: A Survey, IEEE Transactions on Knowledge and Data Engineering, vol.24, issue.5, pp.823-839, 2012.
DOI : 10.1109/TKDE.2010.235
, On The Marriage of Lp-norms and Edit Distance, VLDB, pp.792-803, 2004.
DOI : 10.1016/B978-012088469-8.50070-X
, Robust and Fast Similarity Search for Moving Object Trajectories Erratum to : "Multi-scale anomaly detection algorithm based on infrequent pattern of time series, SIGMOD Conference, pp.491-502, 2005.
, The UCR Time Series Classification Archive OutlierD : an R package for outlier detection using quantile regression on mass spectrometry data, Bioinformatics, vol.44, issue.24 6, pp.56-882, 2008.
, A Cluster Separation Measure, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.1, issue.2, pp.224-227, 0100.
DOI : 10.1109/TPAMI.1979.4766909
, Maximum likelihood from incomplete data via the EM algorithm, JOURNAL OF THE ROYAL STATISTICAL SOCIETY, SERIES B, vol.39, issue.1, pp.1-38, 1977.
, Statistical Comparisons of Classifiers over Multiple Data Sets, Journal of Machine Learning Research, vol.7, issue.59, pp.1-30, 2006.
, Intrusion detection. Applying machine learning to Solaris audit data, Proceedings 14th Annual Computer Security Applications Conference (Cat. No.98EX217), pp.268-299, 1998.
DOI : 10.1109/CSAC.1998.738647
, Modeling system calls for intrusion detection with dynamic window sizes, Proceedings DARPA Information Survivability Conference and Exposition II. DISCEX'01, pp.165-175, 2001.
DOI : 10.1109/DISCEX.2001.932213
, A Geometric Framework for Unsupervised Anomaly Detection, pp.77-101, 2002.
DOI : 10.1007/978-1-4615-0953-0_4
, Time-series data mining, ACM Computing Surveys, vol.45, issue.1, pp.1-1234, 2012.
DOI : 10.1145/2379776.2379788
URL : https://hal.archives-ouvertes.fr/hal-01577883
, FUZZY ROC CURVES FOR THE 1 CLASS SVM : APPLICATION TO INTRUSION DETECTION, p.13
, An introduction to ROC analysis, Pattern Recognition Letters, vol.27, issue.8, pp.861-874, 2006.
DOI : 10.1016/j.patrec.2005.10.010
, Efficient Modeling of Discrete Events for Anomaly Detection Using Hidden Markov Models, Proceedings of the 8th International Conference on Information Security, ISC'05, pp.506-514, 2005.
DOI : 10.1007/11556992_38
, Outliers in Time Series, Journal of the Royal Statistical Society. Series B (Methodological), vol.34, issue.3, pp.350-363, 1972.
, Kostas Gratsias & Yannis Theodoridis. Indexbased Most Similar Trajectory Search, ICDE, pp.816-825, 2007.
, The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance, Journal of the American Statistical Association, vol.32, issue.200, pp.675-701, 1937.
DOI : 10.1080/01621459.1937.10503522
, HMMs (Hidden Markov models) based on anomaly intrusion detection method, Proceedings . International Conference on Machine Learning and Cybernetics, pp.381-385, 2002.
, On community outliers and their efficient detection in information networks, Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '10, pp.813-822, 2010.
DOI : 10.1145/1835804.1835907
, Learning Program Behavior Profiles for Intrusion Detection, Proceedings of the 1st Conference on Workshop on Intrusion Detection and Network Monitoring, pp.6-6, 1999.
, Using Program Behavior Profiles for Intrusion Detection, Proceedings of the SANS Third Conference and Workshop on Intrusion Detection and Response, 1999.
, LOADED: Link-Based Outlier and Anomaly Detection in Evolving Data Sets, Fourth IEEE International Conference on Data Mining (ICDM'04), pp.387-390, 2004.
DOI : 10.1109/ICDM.2004.10011
, A new correlation-based fuzzy logic clustering algorithm for FMRI, Magnetic Resonance in Medicine, vol.5, issue.58, pp.249-260, 1998.
DOI : 10.1007/978-1-4757-0450-1
, A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data, PLOS ONE, vol.27, issue.8, pp.1-31
DOI : 10.1371/journal.pone.0152173.t006
, Anomaly Detection Using Real-Valued Negative Selection. Genetic Programming and Evolvable Machines Towards feature selection in network, CIKM, pp.383-403, 2003.
, Community Trend Outlier Detection Using Soft Temporal Pattern Mining, ECML/PKDD, pp.692-708, 2012.
DOI : 10.1007/978-3-642-33486-3_44
, Integrating community matching and outlier detection for mining evolutionary community outliers, Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '12, pp.859-867, 2012.
DOI : 10.1145/2339530.2339667
, Context-Aware Time Series Anomaly Detection for Complex Systems, Proc. of the SDM Workshop on Data Mining for Service and Maintenance, p.13, 2013.
, Outlier Detection for Temporal Data: A Survey, IEEE Transactions on Knowledge and Data Engineering, vol.26, issue.9, pp.2250-2267
DOI : 10.1109/TKDE.2013.184
, The elements of statistical learning, p.47, 2001.
, Real-time Bayesian anomaly detection in streaming environmental data, Water Resources Research, vol.3, issue.4, p.30, 2009.
DOI : 10.1111/j.1467-9892.1982.tb00349.x
, Anomaly Detection in Streaming Environmental Sensor Data : A Data-driven Modeling Approach, Environ. Model. Softw, vol.25, issue.30, pp.1014-1022, 2010.
, A Survey of Outlier Detection Methodologies, Artif. Intell. Rev, vol.22, issue.2, pp.85-126, 2004.
, Intrusion Detection Using Sequences of System Calls, J. Comput. Secur, vol.6, issue.12, pp.151-180, 1998.
, Mining Deviants in a Time Series Database, VLDB, pp.102-113, 1999.
, Modeling and Tracking of Transaction Flow Dynamics for Fault Detection in Complex Systems, IEEE Transactions on Dependable and Secure Computing, vol.3, issue.4, pp.312-326, 2006.
DOI : 10.1109/TDSC.2006.52
, HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence, Fifth IEEE International Conference on Data Mining (ICDM'05), p.31, 2005.
DOI : 10.1109/ICDM.2005.79
, Ann) Ratanamahatana. Exact indexing of dynamic time warping, Knowl. Inf. Syst, vol.7, issue.3 21, pp.358-386, 2005.
, Finding the most unusual time series subsequence : algorithms and applications, Knowl. Inf. Syst, vol.11, issue.1, pp.1-27, 2007.
, Characterization of network-wide anomalies in traffic flows, Proceedings of the 4th ACM SIGCOMM conference on Internet measurement , IMC '04, pp.201-206, 2004.
DOI : 10.1145/1028788.1028813
, Sequence Matching and Learning in Anomaly Detection for Computer Security, p.13, 1997.
, An Application of Machine Learning to Anomaly Detection Temporal Sequence Learning and Data Reduction for Anomaly Detection, Proceedings of the 20th National Information Systems Security Conference, pp.366-380, 1997.
, Generic and Scalable Framework for Automated Time-series Anomaly Detection, Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '15, pp.1939-1947, 2015.
DOI : 10.1080/00401706.1983.10487848
, Radar: Residual Analysis for Anomaly Detection in Attributed Networks, Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, pp.2152-2158, 2017.
DOI : 10.24963/ijcai.2017/299
, A symbolic representation of time series, with implications for streaming algorithms, Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery , DMKD '03, pp.2-11, 2003.
DOI : 10.1145/882082.882086
, Isolation Forest, ICDM, pp.413-422, 2008.
, Time-series novelty detection using one-class support vector machines, Proceedings of the International Joint Conference on Neural Networks, 2003., pp.1741-1745, 2003.
DOI : 10.1109/IJCNN.2003.1223670
, Online novelty detection on temporal sequences, Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '03, pp.613-618, 2003.
DOI : 10.1145/956750.956828
, 1d-SAX: A Novel Symbolic Representation for Time Series, IDA, pp.273-284, 2013.
DOI : 10.1007/978-3-642-41398-8_24
URL : https://hal.archives-ouvertes.fr/halshs-00912512
, Characterizing the Behavior of a Program Using Multiple-length N-grams, Proceedings of the 2000 Workshop on New Security Paradigms, NSPW '00, pp.101-110, 2000.
, Dictionarydecomposition-based one-class svm for unsupervised detection of anomalous time series Two state-based approaches to programbased anomaly detection, Proceedings of 23rd European Signal Processing Conference (EUSIPCO) Computer Security Applications ACSAC '00. 16th Annual Conference, pp.1776-1780, 2000.
, Fuzzy Clustering of Short Time-Series and Unevenly Distributed Sampling Points, IDA, pp.330-340, 2003.
DOI : 10.1007/978-3-540-45231-7_31
, Distance Measures for Time Series in R : The TSdist Package, R journal, vol.8, issue.22, pp.451-459, 1920.
, A System for the Analysis of Jet Engine Vibration Data, Integrated Computer-Aided Engineering, vol.6, issue.13, pp.53-66, 1999.
, On Spectral Clustering : Analysis and an algorithm Efficient and Robust Feature Selection via Joint 2,1 -Norms Minimization Fast Distributed Outlier Detection in Mixed-Attribute Data Sets Ganesha : blackBox diagnosis of MapReduce systems, NIPS NIPS, pp.849-856, 2001.
, Intrusion Detection with Unlabeled Data Using Clustering Anomaly intrusion detection method based on HMM, Electronics Letters, vol.11, issue.38 13, pp.58-663, 2001.
, Making Time-series Classification More Accurate Using Learned Constraints, SDM, pp.11-22, 2004.
DOI : 10.1137/1.9781611972740.2
, Finding anomalous periodic time series, Machine Learning, pp.281-313, 2009.
DOI : 10.1017/CBO9780511564796
, Dynamic programming algorithm optimization for spoken word recognition, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.26, issue.1, pp.43-49, 1921.
DOI : 10.1109/TASSP.1978.1163055
, Learning States and Rules for Detecting Anomalies in Time Series, Applied Intelligence, vol.23, issue.3, pp.241-255, 2005.
, Support Vector Method for Novelty Detection, Proceedings of the 12th International Conference on Neural Information Processing Systems, NIPS'99, pp.582-588, 1999.
, Learning with kernels : Support vector machines, regularization, optimization, and beyond, p.57, 2001.
, ADMIT : anomalybased data mining for intrusions, Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.386-395, 2002.
, TSA-tree: a wavelet-based approach to improve the efficiency of multi-level surprise and trend queries on time-series data, Proceedings. 12th International Conference on Scientific and Statistica Database Management, pp.55-68, 2000.
DOI : 10.1109/SSDM.2000.869778
, Outlier Detection Using Nonconvex Penalized Regression, Journal of the American Statistical Association, vol.106, issue.76, pp.626-639, 2011.
, Fault detection using neural networks, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94), pp.3796-3799, 1994.
DOI : 10.1109/ICNN.1994.374815
, Kernels and Regularization on Graphs Mining for Outliers in Sequential Databases, COLT, volume 2777 of Lecture Notes in Computer ScienceSun06] Pei Sun SDM, pp.144-158, 2003.
, Recursive data mining for masquerade detection and author identification CoSelect : Feature Selection with Instance Selection for Social Media Data, Proceedings from the Fifth Annual IEEE SMC Information Assurance Workshop SDM, pp.424-431, 2004.
, Speech discrimination by dynamic programming, Cybernetics, vol.4, issue.1, pp.52-57, 1968.
DOI : 10.1007/BF01074755
, Discovering similar multidimensional trajectories, Proceedings 18th International Conference on Data Engineering, pp.673-684, 2002.
DOI : 10.1109/ICDE.2002.994784
, Clustering Time Series Data â A Survey, pp.1857-1874, 1925.
, Detecting intrusions using system calls: alternative data models, Proceedings of the 1999 IEEE Symposium on Security and Privacy (Cat. No.99CB36344), pp.133-145, 1999.
DOI : 10.1109/SECPRI.1999.766910
, Stefano Lonardi & Chotirat (Ann) Ratanamahatana. Assumption-Free Anomaly Detection in Time Series, SSDBM, pp.237-240, 2005.
, SAXually Explicit Images : Finding Unusual Shapes, Sixth International Conference on Data Mining (ICDM'06), pp.711-720, 2006.
, Tiresias: Black-Box Failure Prediction in Distributed Systems, 2007 IEEE International Parallel and Distributed Processing Symposium, pp.1-8, 2007.
DOI : 10.1109/IPDPS.2007.370345
, CLUSEQ: efficient and effective sequence clustering, Proceedings 19th International Conference on Data Engineering (Cat. No.03CH37405), pp.101-112, 2003.
DOI : 10.1109/ICDE.2003.1260785
, Disk aware discord discovery: finding unusual time series in terabyte sized datasets, Proceedings of the 2000 IEEE Workshop on Information Assurance and Security, pp.241-262, 2000.
DOI : 10.1007/978-1-4899-3324-9
, Parzen-window network intrusion detectors . In Object recognition supported by user interaction for service robots, pp.385-388, 2002.
, Detecting spatio-temporal outliers in climate dataset: a method study, Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05., pp.4-30, 2005.
DOI : 10.1109/IGARSS.2005.1525218
, Spark : Cluster Computing with Working Sets, Proceedings of the 2Nd USENIX Conference on Hot Topics in Cloud Computing, HotCloud'10, pp.10-10, 2010.
, A new anomaly detection method based on hierarchical HMM, Proceedings of the Fourth International Conference on Parallel and Distributed Computing, Applications and Technologies, pp.249-252, 2003.
, Outlier Detection Techniques for Wireless Sensor Networks: A Survey, IEEE Communications Surveys & Tutorials, vol.12, issue.2, pp.159-170, 2010.
DOI : 10.1109/SURV.2010.021510.00088
, Learning from labeled and unlabeled data on a directed graph, Proceedings of the 22nd international conference on Machine learning , ICML '05, pp.1036-1043, 2005.
DOI : 10.1145/1102351.1102482
URL : http://www.kyb.tuebingen.mpg.de/publications/attachments/LPDG_3463%5B1%5D.pdf