Anomaly detection

Results: 503



#Item
51Towards Detecting Anomalous User Behavior in Online Social Networks Bimal Viswanath MPI-SWS Krishna P. Gummadi MPI-SWS

Towards Detecting Anomalous User Behavior in Online Social Networks Bimal Viswanath MPI-SWS Krishna P. Gummadi MPI-SWS

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Source URL: www.mpi-sws.org

Language: English - Date: 2014-07-14 10:42:22
52An Incremental Learner for Language-Based Anomaly Detection in XML Harald Lampesberger Department of Secure Information Systems University of Applied Sciences Upper Austria

An Incremental Learner for Language-Based Anomaly Detection in XML Harald Lampesberger Department of Secure Information Systems University of Applied Sciences Upper Austria

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Source URL: spw16.langsec.org

Language: English - Date: 2016-06-05 23:39:24
53Microsoft PowerPoint - ALERT_poster_JWang.pptx

Microsoft PowerPoint - ALERT_poster_JWang.pptx

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Source URL: www.northeastern.edu

Language: English - Date: 2012-07-09 12:22:54
54AUTOMATIC OUTLIER DETECTION IN MUSIC GENRE DATASETS Yen-Cheng Lu1 Chih-Wei Wu2  Chang-Tien Lu1

AUTOMATIC OUTLIER DETECTION IN MUSIC GENRE DATASETS Yen-Cheng Lu1 Chih-Wei Wu2 Chang-Tien Lu1

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Source URL: m.mr-pc.org

Language: English - Date: 2016-07-29 14:11:56
55Sequential	Feature	Explanations	for	 Anomaly	Detection	 Md	Amran	Siddiqui,	Alan	Fern,	Thomas	G.	Die8erich	and Weng-Keen	Wong	  School	of	EECS

Sequential Feature Explanations for Anomaly Detection Md Amran Siddiqui, Alan Fern, Thomas G. Die8erich and Weng-Keen Wong School of EECS

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Source URL: intelligence.org

Language: English - Date: 2016-05-31 11:21:14
56Using Data Mining Techniques for Detecting Terror-Related Activities on the Web Y.Elovici1, A.Kandel2, M.Last1, B.Shapira1, O. Zaafrany1 1

Using Data Mining Techniques for Detecting Terror-Related Activities on the Web Y.Elovici1, A.Kandel2, M.Last1, B.Shapira1, O. Zaafrany1 1

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Source URL: www.ise.bgu.ac.il

Language: English - Date: 2004-08-02 11:37:04
57Data Driven Investigation of Faults in HVAC Systems with Model, Cluster and Compare (MCC) Balakrishnan Narayanaswamy† , Bharathan Balaji† , Rajesh Gupta† , Yuvraj Agarwal†‡ † University  ‡ Carnegie

Data Driven Investigation of Faults in HVAC Systems with Model, Cluster and Compare (MCC) Balakrishnan Narayanaswamy† , Bharathan Balaji† , Rajesh Gupta† , Yuvraj Agarwal†‡ † University ‡ Carnegie

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Source URL: mesl.ucsd.edu

Language: English - Date: 2015-03-04 00:04:56
58Finding large near-cliques in massive networks is a notoriously hard problem of great importance to many applications, including anomaly detection in security, community detection in social networks, and mining the Web g

Finding large near-cliques in massive networks is a notoriously hard problem of great importance to many applications, including anomaly detection in security, community detection in social networks, and mining the Web g

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Source URL: mmds-data.org

- Date: 2016-06-23 15:50:48
    59SYSTEMATIC CONSTRUCTION OF ANOMALY DETECTION BENCHMARKS FROM REAL DATA Outlier Detection And Description Workshop 2013

    SYSTEMATIC CONSTRUCTION OF ANOMALY DETECTION BENCHMARKS FROM REAL DATA Outlier Detection And Description Workshop 2013

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    Source URL: www.outlier-analytics.org

    Language: English - Date: 2013-09-06 10:43:41
    60Detecting Changes in a Dynamic Social Network Ian McCulloh March 31, 2009 CMU-ISR

    Detecting Changes in a Dynamic Social Network Ian McCulloh March 31, 2009 CMU-ISR

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    Source URL: www.casos.cs.cmu.edu

    Language: English - Date: 2010-01-18 19:22:21