The curse of highly variable functions for local kernel machines, Proc. of the 18th Int. Conf. on Neural Information Processing Systems, pp.107-114, 2005. ,
Impact of packet sampling on anomaly detection metrics, Proceedings of the 6th ACM SIGCOMM on Internet measurement , IMC '06, pp.159-164, 2006. ,
DOI : 10.1145/1177080.1177101
NETWORKING 2011: 10th Int. IFIP TC 6 Networking Conf., chapter UNADA: Unsupervised Network Anomaly Detection Using Sub-space Outliers Ranking, pp.40-51 ,
Unsupervised Network Intrusion Detection Systems: Detecting the Unknown without Knowledge, Computer Communications, vol.35, issue.7, pp.772-783, 2012. ,
DOI : 10.1016/j.comcom.2012.01.016
URL : https://hal.archives-ouvertes.fr/hal-00736278
Predictive Network Anomaly Detection and Visualization, IEEE Transactions on Information Forensics and Security, vol.5, issue.2, pp.288-299, 2010. ,
DOI : 10.1109/TIFS.2010.2041808
Clustering Spam Campaigns with Fuzzy Hashing, Proceedings of the AINTEC 2014 on Asian Internet Engineering Conference, AINTEC '14, p.66, 2014. ,
DOI : 10.1016/j.diin.2006.06.015
An incremental grid density-based clustering algorithm, Journal of Software, vol.13, issue.1, 2002. ,
Unsupervised Network Anomaly Detection in Real-Time on Big Data, New Trends in Databases and Information Systems, pp.197-206 ,
DOI : 10.1007/978-3-319-23201-0_22
URL : https://hal.archives-ouvertes.fr/hal-01229003
The MINDS -Minnesota Intrusion Detection System, Next Generation Data Mining, 2004. ,
A density-based algorithm for discovering clusters in large spatial databases with noise, Proc. of the Second Int. Conf. on Knowledge Discovery and Data Mining, pp.226-231, 1996. ,
Taming the Curse of Dimensionality in Kernels and Novelty Detection, pp.425-438, 2006. ,
DOI : 10.1007/3-540-31662-0_33
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
Online adaptive anomaly detection for augmented network flows, IEEE 22nd Int. Symp. on Modelling, pp.433-442, 2014. ,
DOI : 10.1145/2934686
Anomaly intrusion detection system using gaussian mixture model Stoecklin, and X. Dimitropoulos. Histogram-based traffic anomaly detection, Int. Conf. on Convergence Inform. Technol. IEEE Trans. on Network and Service Management, vol.01, issue.62, pp.1162-1167110, 2008. ,
The R*-tree: an efficient and robust access method for points and rectangles, Sigmod Record, vol.19, pp.322-331, 1990. ,
Mining anomalies using traffic feature distributions, ACM SIGCOMM Computer Communication Review, vol.35, issue.4, p.217, 2005. ,
DOI : 10.1145/1090191.1080118
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.116.4156
Diagnosing network-wide traffic anomalies, Conf. on Applications, Technologies, Architectures, and Protocols for Comput. Commun, pp.219-230, 2004. ,
DOI : 10.1145/1030194.1015492
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1.1838
Unsupervised anomaly detection in network intrusion detection using clusters, Twenty-Eighth Australasian Comput. Science Conf. (ACSC2005), pp.333-342, 2005. ,
Network Anomaly Detection by Cascading K-Means Clustering and C4.5 Decision Tree algorithm, Procedia Engineering, vol.30, pp.174-182, 2012. ,
DOI : 10.1016/j.proeng.2012.01.849
URL : http://doi.org/10.1016/j.proeng.2012.01.849
Signal Processing-based Anomaly Detection Techniques: A Comparative Analysis, INTERNET 2011, pp.32-39, 2011. ,
URL : https://hal.archives-ouvertes.fr/hal-00626901
An overview of anomaly detection techniques: Existing solutions and latest technological trends, Computer Networks, vol.51, issue.12, 2007. ,
DOI : 10.1016/j.comnet.2007.02.001
Intrusion detection with unlabeled data using clustering, Proc. of ACM CSS Workshop on Data Mining Applied to Security (DMSA), 2001. ,
Finding a "Kneedle" in a Haystack: Detecting Knee Points in System Behavior, 2011 31st International Conference on Distributed Computing Systems Workshops, pp.166-171, 2011. ,
DOI : 10.1109/ICDCSW.2011.20
A novel anomaly detection scheme based on principal component classifier, IEEE Foundations and New Directions of Data Mining Workshop, pp.171-179, 2003. ,
The Proof and Measurement of Association between Two Things, The American Journal of Psychology, vol.15, issue.1, pp.72-101, 1904. ,
DOI : 10.2307/1412159
The anomaly detection by using dbscan clustering with multiple parameters, Information Science and Applications (ICISA), pp.1-5, 2011. ,
Anomaly detection in IP networks, IEEE Transactions on Signal Processing, vol.51, issue.8, pp.2191-2204, 2003. ,
DOI : 10.1109/TSP.2003.814797
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.94.4019