{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T16:01:27Z","timestamp":1772726487646,"version":"3.50.1"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2015,5,20]],"date-time":"2015-05-20T00:00:00Z","timestamp":1432080000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Pattern Anal Applic"],"published-print":{"date-parts":[[2017,2]]},"DOI":"10.1007\/s10044-015-0484-0","type":"journal-article","created":{"date-parts":[[2015,5,19]],"date-time":"2015-05-19T05:52:58Z","timestamp":1432014778000},"page":"183-199","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["A fast and noise resilient cluster-based anomaly detection"],"prefix":"10.1007","volume":"20","author":[{"given":"Elnaz","family":"Bigdeli","sequence":"first","affiliation":[]},{"given":"Mahdi","family":"Mohammadi","sequence":"additional","affiliation":[]},{"given":"Bijan","family":"Raahemi","sequence":"additional","affiliation":[]},{"given":"Stan","family":"Matwin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2015,5,20]]},"reference":[{"key":"484_CR1","unstructured":"Han J, Kamber M, Pei J (2011) Data mining: concepts and techniques, 3rd edn. The Morgan Kaufmann series in data management systems"},{"issue":"3","key":"484_CR2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1541880.1541882","volume":"41","author":"V Chandola","year":"2009","unstructured":"Chandola V, Banerjee A, Kumar V (2009) Anomaly detection: a survey. ACM Comput Surv (CSUR) 41(3):1\u201358","journal-title":"ACM Comput Surv (CSUR)"},{"key":"484_CR3","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.cose.2009.06.008","volume":"29","author":"CV Zhou","year":"2009","unstructured":"Zhou CV, Leckie C, Karunasekera S (2009) A survey of coordinated attacks and collaborative intrusion detection. Comput Secur 29:124\u2013140","journal-title":"Comput Secur"},{"key":"484_CR4","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.cose.2008.08.003","volume":"28","author":"PG Teodoro","year":"2009","unstructured":"Teodoro PG, Verdejo JED, Fern\u00e1ndez GM, V\u00e1zquez E (2009) Anomaly-based network intrusion detection: techniques, systems and challenges. Comput Secur 28:18\u201328","journal-title":"Comput Secur"},{"key":"484_CR5","doi-asserted-by":"crossref","unstructured":"Beusekom JV, Shafait F (2011) Distortion measurement for automatic document verification. In: International conference on document analysis and recognition (ICDAR), 2011","DOI":"10.1109\/ICDAR.2011.66"},{"key":"484_CR6","doi-asserted-by":"crossref","unstructured":"Lin J, Keogh E, Herle HV (2005) Approximations to magic: finding unusual medical time series. In: Proceedings of the 18th IEEE symposium on computer-based medical systems, 2005","DOI":"10.1109\/CBMS.2005.34"},{"key":"484_CR7","unstructured":"Sajja PS, Akerkar R (2010) Knowledge-based systems for development. Advanced Knowledge Based Systems: Model, Applications & Research 1\u201311"},{"issue":"5","key":"484_CR8","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1007\/s12065-013-0101-3","volume":"6","author":"M Mohammadi","year":"2014","unstructured":"Mohammadi M, Akbari A, Raahemi B, Nasersharif B, Asgharian H (2014) A fast anomaly detection system using probabilistic artificial immune algorithm capable of learning new attacks. Evol Intel 6(5):135\u2013156","journal-title":"Evol Intel"},{"key":"484_CR9","unstructured":"Smith R, Bivens A, Embrechits M, Palagiri C, Szymanski B (2002) Clustering approaches for anomaly-based intrusion detection. In: Proceedings of intelligent engineering systems through artificial neural networks"},{"issue":"5","key":"484_CR10","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1109\/TNN.2005.853414","volume":"16","author":"H Hajji","year":"2005","unstructured":"Hajji H (2005) Statistical analysis of network traffic for adaptive faults detection. Trans Neural Netw 16(5):1053\u20131063","journal-title":"Trans Neural Netw"},{"key":"484_CR11","doi-asserted-by":"crossref","unstructured":"Ndousse TD, Okuda T (1996) Computational intelligence for distributed fault management in networks using fuzzy cognitive maps. In: 1996 IEEE international conference on communications, 1996. ICC \u201896, Conference Record, Converging Technologies for Tomorrow\u2019s Applications, Dallas, TX","DOI":"10.1109\/ICC.1996.533672"},{"key":"484_CR12","doi-asserted-by":"crossref","unstructured":"Brause R, Langsdorf T (1999) Neural data mining for credit card fraud detection. In: Proceedings of the 11th IEEE international conference on tools with artificial intelligence, 1999","DOI":"10.1109\/TAI.1999.809773"},{"key":"484_CR13","doi-asserted-by":"crossref","unstructured":"Tandon G, Chan P (2007) Weighting versus pruning in rule validation for detecting network and host anomalies. In: KDD \u201807 proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining","DOI":"10.1145\/1281192.1281267"},{"issue":"2","key":"484_CR14","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1109\/TNET.2010.2070845","volume":"19","author":"G Thatte","year":"2011","unstructured":"Thatte G, Mitra U, Heidemann J (2011) Parametric methods for anomaly detection in aggregate traffic. IEEE\/ACM Trans Netw 19(2):512\u2013525","journal-title":"IEEE\/ACM Trans Netw"},{"key":"484_CR15","doi-asserted-by":"crossref","unstructured":"Leng M, Lai X, Tan G, Xu X (2009) Time series representation for anomaly detection. In: 2nd IEEE international conference on computer science and information technology, 2009 (ICCSIT 2009)","DOI":"10.1109\/ICCSIT.2009.5234775"},{"key":"484_CR16","unstructured":"Sricharan K, Hero AO (2011) Efficient anomaly detection using bipartite k-nn graphs. In: Proceedings of advances in neural information processing systems (NIPS)"},{"issue":"1\u20132","key":"484_CR17","doi-asserted-by":"crossref","first-page":"1469","DOI":"10.14778\/1920841.1921021","volume":"3","author":"GH Orair","year":"2010","unstructured":"Orair GH, Teixeira CHC, Meira W, Wang JY, Parthasarathy S (2010) Distance-based outlier detection: consolidation and renewed bearing. Proc VLDB Endow 3(1\u20132):1469\u20131480","journal-title":"Proc VLDB Endow"},{"key":"484_CR18","doi-asserted-by":"crossref","first-page":"1661","DOI":"10.1109\/TPDS.2012.261","volume":"24","author":"M Xie","year":"2013","unstructured":"Xie M, Hu J, Han S, Chen HH (2013) Scalable hyper-grid k-NN-based online anomaly detection in wireless sensor networks. IEEE Trans Parallel Distrib Syst 24:1661\u20131670","journal-title":"IEEE Trans Parallel Distrib Syst"},{"key":"484_CR19","doi-asserted-by":"crossref","unstructured":"Boriah S, Chandola V, Kumar V (2008) Similarity measures for categorical data: a comparative evaluation. In: Proceedings of the eighth SIAM international conference on data mining","DOI":"10.1137\/1.9781611972788.22"},{"key":"484_CR20","doi-asserted-by":"crossref","unstructured":"Breunig MM, Kriegel HP, Ng TR, Sander J (2000) LOF: identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD international conference on management of data","DOI":"10.1145\/342009.335388"},{"issue":"1","key":"484_CR21","doi-asserted-by":"crossref","first-page":"622","DOI":"10.14778\/1687627.1687698","volume":"2","author":"MS Kim","year":"2009","unstructured":"Kim MS, Han J (2009) A particle-and-density based evolutionary clustering method for dynamic networks. Proc VLDB Endow 2(1):622\u2013633","journal-title":"Proc VLDB Endow"},{"key":"484_CR22","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1162\/089976601750264965","volume":"13","author":"B Scholkopf","year":"2001","unstructured":"Scholkopf B, Platt JC, Taylor JS, Smola AJ, Williamson RC (2001) Estimating the support of a high dimensional distribution. Neural Comput 13:1443\u20131471","journal-title":"Neural Comput"},{"key":"484_CR23","first-page":"1493","volume":"7","author":"SS Keerthi","year":"2006","unstructured":"Keerthi SS, Chapelle O, DeCoste D (2006) Building support vector machines with reduced classifier complexity. J Mach Learn Res 7:1493\u20131515","journal-title":"J Mach Learn Res"},{"key":"484_CR24","doi-asserted-by":"crossref","unstructured":"Amer M, Goldstein M, Abdennadher S (2013) Enhancing one-class support vector machines for unsupervised anomaly detection. In: Proceeding ODD \u201813 proceedings of the ACM SIGKDD workshop on outlier detection and description","DOI":"10.1145\/2500853.2500857"},{"key":"484_CR25","doi-asserted-by":"crossref","unstructured":"Chen Y, Qian J, Saligrama V (2013) A new one-class SVM for anomaly detection. In: 2013 IEEE international conference on acoustics, speech and signal processing (ICASSP)","DOI":"10.1109\/ICASSP.2013.6638322"},{"issue":"18","key":"484_CR26","doi-asserted-by":"crossref","first-page":"3799","DOI":"10.1016\/j.ins.2007.03.025","volume":"177","author":"T Shon","year":"2007","unstructured":"Shon T, Moon J (2007) A hybrid machine learning approach to network anomaly detection. Inf Sci 177(18):3799\u20133821","journal-title":"Inf Sci"},{"issue":"3","key":"484_CR27","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1109\/TSMCB.2005.860136","volume":"36","author":"JS Han","year":"2006","unstructured":"Han JS, Cho SB (2006) Evolutionary neural networks for anomaly detection based on the behavior of a program. IEEE Trans Syst Man Cybern B Cybern 36(3):559\u2013570","journal-title":"IEEE Trans Syst Man Cybern B Cybern"},{"key":"484_CR28","unstructured":"Ester M, Kriegel H, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of 2nd international conference on knowledge discovery and data mining (KDD-96)"},{"issue":"9\u201310","key":"484_CR29","doi-asserted-by":"crossref","first-page":"1641","DOI":"10.1016\/S0167-8655(03)00003-5","volume":"24","author":"Z He","year":"2003","unstructured":"He Z, Xu X, Deng S (2003) Discovering cluster-based local outliers. Pattern Recog Lett 24(9\u201310):1641\u20131650","journal-title":"Pattern Recog Lett"},{"key":"484_CR30","unstructured":"Wang W, Yang J, Muntz RR (1997) Sting: a statistical information grid approach to spatial data mining. In: Proceeding VLDB \u201897 proceedings of the 23rd international conference on very large data bases, San Francisco"},{"key":"484_CR31","doi-asserted-by":"crossref","unstructured":"Agrawal J, Gunopulos D, Raghavan P (1998) Automatic sub-space clustering of high dimensional data for data mining applications. In: Proceedings of the 1998 ACM SIGMOD international conference on management of data","DOI":"10.1145\/276304.276314"},{"key":"484_CR32","unstructured":"Kersting K, Wahabzada M, Thurau C, Bauckhage C (2010) Hierarchical convex NMF for clustering massive data. In: Machine Learning Research\u2014Proceedings Track, pp 253\u2013268"},{"key":"484_CR33","first-page":"4","volume":"13","author":"J Hershberger","year":"2009","unstructured":"Hershberger J, Shrivastava N, Suri S (2009) Summarizing spatial data streams using ClusterHulls. J Exp Algorithm (JEA) 13:4","journal-title":"J Exp Algorithm (JEA)"},{"issue":"3","key":"484_CR34","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1109\/TKDE.2007.44","volume":"19","author":"S Gaddam","year":"2007","unstructured":"Gaddam S, Phoha V, Balagani K (2007) K-means+id3: a novel method for supervised anomaly detection by cascading k-means clustering and id3 decision tree learning methods. IEEE Trans Knowl Data Eng 19(3):345\u2013354","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"484_CR35","doi-asserted-by":"crossref","unstructured":"Cao F, Ester M, Qian W, Zhou A (2006) Density-based clustering over an evolving data stream with noise. In: Proceeding of SIAM conference on data mining","DOI":"10.1137\/1.9781611972764.29"},{"issue":"1","key":"484_CR36","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1214\/aoms\/1177729694","volume":"22","author":"S Kullback","year":"1951","unstructured":"Kullback S, Leibler RA (1951) On information and sufficiency. Ann Math Stat 22(1):79\u201386","journal-title":"Ann Math Stat"},{"key":"484_CR37","doi-asserted-by":"crossref","unstructured":"Goldberger J, Gordon S Greenspan H (2003) An efficient image similarity measure based on approximations of kl divergence between two gaussian mixtures. In: Proceedings of the ninth IEEE international conference on computer vision","DOI":"10.1109\/ICCV.2003.1238387"},{"key":"484_CR38","doi-asserted-by":"crossref","unstructured":"Hershey J, Olsen P (2007) Approximating the Kullback Leibler divergence between Gaussian mixture models. In: IEEE international conference on acoustics, speech and signal processing, 2007 (ICASSP 2007)","DOI":"10.1109\/ICASSP.2007.366913"},{"key":"484_CR39","doi-asserted-by":"crossref","unstructured":"Chaoji V, Li G, Yildirim H, Zaki MJ (2011) Mining arbitrary shaped clusters from large datasets based on backbone identification. In: SDM 2011","DOI":"10.1137\/1.9781611972818.26"},{"key":"484_CR40","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1080\/01969727408546059","volume":"4","author":"K Dunn","year":"1997","unstructured":"Dunn K, Dunn J (1997) Well separated clusters and optimal fuzzy partitions. Cybernetics 4:95\u2013104","journal-title":"Cybernetics"},{"issue":"4","key":"484_CR41","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1109\/TPAMI.1979.4766909","volume":"1","author":"LD Davies","year":"1979","unstructured":"Davies LD, Bouldin WD (1979) A cluster separation measure. IEEE Trans Pattern Anal Mach Intell 1(4):224\u2013227","journal-title":"IEEE Trans Pattern Anal Mach Intell"}],"container-title":["Pattern Analysis and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-015-0484-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10044-015-0484-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-015-0484-0","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-015-0484-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,25]],"date-time":"2019-08-25T08:07:06Z","timestamp":1566720426000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10044-015-0484-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,5,20]]},"references-count":41,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2017,2]]}},"alternative-id":["484"],"URL":"https:\/\/doi.org\/10.1007\/s10044-015-0484-0","relation":{},"ISSN":["1433-7541","1433-755X"],"issn-type":[{"value":"1433-7541","type":"print"},{"value":"1433-755X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,5,20]]}}}