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Maximally Divergent Intervals for Anomaly Detection [article]

Erik Rodner, Björn Barz, Yanira Guanche, Milan Flach, Miguel Mahecha, Paul Bodesheim, Markus Reichstein, Joachim Denzler
2016 pre-print
We present new methods for batch anomaly detection in multivariate time series.  ...  Our methods are based on maximizing the Kullback-Leibler divergence between the data distribution within and outside an interval of the time series.  ...  Acknowledgements The support of the project EU H2020-EO-2014 project BACI 'Detecting changes in essential ecosystem and biodiversity properties-towards a Biosphere Atmosphere Change Index, contract 640176  ... 
doi:10.17871/baci_icml2016_rodner arXiv:1610.06761v1 fatcat:nlorjjylonfz3lxoyacocaik3q

Detecting Regions of Maximal Divergence for Spatio-Temporal Anomaly Detection

Bjorn Barz, Erik Rodner, Yanira Guanche Garcia, Joachim Denzler
2018 IEEE Transactions on Pattern Analysis and Machine Intelligence  
In opposition to existing techniques for detecting isolated anomalous data points, we propose the "Maximally Divergent Intervals" (MDI) framework for unsupervised detection of coherent spatial regions  ...  Automatic detection of anomalies in space- and time-varying measurements is an important tool in several fields, e.g., fraud detection, climate analysis, or healthcare monitoring.  ...  Detection #3: words 7347 -7684 (Score: 39679.642) may not understand one another ' s speech .  ... 
doi:10.1109/tpami.2018.2823766 pmid:29993434 fatcat:opqloied2zewzebxfm6tqlrzq4

Anomaly based Real Time Prevention of under Rated App-DDOS Attacks on Web: An Experiential Metrics based Machine Learning Approach

K. Munivara Prasad, A. Rama Mohan Reddy, K. Venugopal Rao
2016 Indian Journal of Science and Technology  
To devise an Anomaly based Real Time Prevention (ARTP) of under rated App-DDOS attacks on Web for achieving fast and early detection.  ...  time frame discovered to identify the anomalies of the metrics proposed.  ...  The devised ARTP amplified the detection accuracy with minimal process complexity and maximal speed.  ... 
doi:10.17485/ijst/2016/v9i27/87872 fatcat:qfoh6y24uvbwdhmqly5hpv3rxq

ReAD: A Regional Anomaly Detection Framework Based on Dynamic Partition [article]

Huaishao Luo, Chuishi Meng, Bowen Wu, Junbo Zhang, Tianrui Li, Yu Zheng
2020 arXiv   pre-print
Then, an anomaly metric will be calculated for each region by a regional divergence calculation method.  ...  Besides, we proposed an unsupervised REgional Anomaly Detection framework (ReAD) to detect abnormal regions with arbitrary shapes by jointly considering spatial and temporal properties.  ...  MDI is a Maximally Divergent Intervals algorithm [13] . Besides the baselines, we also show the effectiveness of both weighted approach and wavy approach of the ReAD.  ... 
arXiv:2007.06794v2 fatcat:uxadpp2mifb75gcigr3o2kdvhu

A Novel Hybrid Method for KPI Anomaly Detection Based on VAE and SVDD

Yun Zhao, Xiuguo Zhang, Zijing Shang, Zhiying Cao
2021 Symmetry  
In the SVDD anomaly detection module, smoothed reconstruction errors are introduced into the SVDD for training to determine the threshold of adaptively anomaly detection.  ...  Key performance indicator (KPI) anomaly detection is the underlying core technology in Artificial Intelligence for IT operations (AIOps).  ...  Acknowledgments: The authors would like to thank all anonymous reviewers and editors for their helpful suggestions for the improvement of this paper.  ... 
doi:10.3390/sym13112104 fatcat:unvq2ae2r5g37h7vljwzslunqu

Detecting anomalies in dynamic rating data

Stephan Günnemann, Nikou Günnemann, Christos Faloutsos
2014 Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '14  
In this work, we tackle the following question: Given the time stamped rating data for a product or service, how can we detect the general rating behavior of users as well as time intervals where the ratings  ...  To capture the dynamic effects of the ratings, the categorical mixtures are temporally constrained: Anomalies can occur in specific time intervals only and the general rating behavior should evolve smoothly  ...  Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.  ... 
doi:10.1145/2623330.2623721 dblp:conf/kdd/GunnemannGF14 fatcat:7t2ayikfu5ftpnz5mv747uvyt4

FLEAD

Viet Duc Le, Hans Scholten, Paul Havinga
2013 Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication - UbiComp '13 Adjunct  
However, most conventional anomaly detection methods are power hungry and computation consuming.  ...  One way to do this is estimating the event probability based on anomaly detection to invoke heavy processes, such as switching on more sensors or retrieving information.  ...  Table 4 : 4 Edge detection for 10-minute anomaly intervals. Table 5 : 5 Pulse width detection for 1-minute anomaly intervals.  ... 
doi:10.1145/2494091.2499774 dblp:conf/huc/LeSH13 fatcat:6lj5xfvqr5b2pej54dw2vshoge

Addressing Variance Shrinkage in Variational Autoencoders using Quantile Regression [article]

Haleh Akrami, Anand A. Joshi, Sergul Aydore, Richard M. Leahy
2020 arXiv   pre-print
VAEs can therefore use reconstruction probability instead of reconstruction error for anomaly detection.  ...  Recently, the probabilistic Variational AutoEncoder (VAE) has become a popular model for anomaly detection in applications such as lesion detection in medical images.  ...  VAEs are popular for anomaly detection.  ... 
arXiv:2010.09042v1 fatcat:47xgrvwokvbw7cu3bakhy7x77q

Statistical Traffic Anomaly Detection in Time-Varying Communication Networks

Jing Wang, Ioannis Ch. Paschalidis
2015 IEEE Transactions on Control of Network Systems  
We propose two methods for traffic anomaly detection in communication networks where properties of normal traffic evolve dynamically.  ...  We formulate the anomaly detection problem as a binary composite hypothesis testing problem and develop a model-free and a model-based method, leveraging techniques from the theory of large deviations.  ...  SIMULATION RESULTS Lacking data with annotated anomalies is a common problem for validation of anomaly detection methods.  ... 
doi:10.1109/tcns.2014.2378631 fatcat:jc3wy6wnrfgmtlez66urw3t2oy

Visual anomaly detection under temporal and spatial non-uniformity for news finding robot

T. Suzuki, F. Bessho, T. Harada, Y. Kuniyoshi
2011 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems  
In this paper, we propose a news-gathering mobile robot system, and the novel visual anomaly detection method as the core function of news detection in the real world.  ...  Visual anomaly detection is important and widely applicable not only to the news-gathering robot but also to the security systems.  ...  Some studies have used non-stationary cameras for anomaly detection [6] .  ... 
doi:10.1109/iros.2011.6094719 dblp:conf/iros/SuzukiBHK11 fatcat:xbgjf3htzffido7gb25suwdfx4

Visual anomaly detection under temporal and spatial non-uniformity for news finding robot

Takahiro Suzuki, Fumihiro Bessho, Tatsuya Harada, Yasuo Kuniyoshi
2011 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems  
In this paper, we propose a news-gathering mobile robot system, and the novel visual anomaly detection method as the core function of news detection in the real world.  ...  Visual anomaly detection is important and widely applicable not only to the news-gathering robot but also to the security systems.  ...  Some studies have used non-stationary cameras for anomaly detection [6] .  ... 
doi:10.1109/iros.2011.6048327 fatcat:45dii2njsbgsvgka2j7n5dvvga

A Divergence-measure Based Classification Method for Detecting Anomalies in Network Traffic

Kiran S. Balagani, Vir V. Phoha, Gopi K. Kuchimanchi
2007 2007 IEEE International Conference on Networking, Sensing and Control  
We present 'D-CAD,' a novel divergence-measure based classification method for anomaly detection in network traffic.  ...  detection method and to an AUC value of 0.8887 of the supervised Bayesian estimation based anomaly detection method.  ...  In this paper we develop a novel classification method for anomaly detection called the Divergence-measure based Classification for Anomaly Detection (D-CAD), which uses divergence measure [20] from  ... 
doi:10.1109/icnsc.2007.372808 dblp:conf/icnsc/BalaganiPK07 fatcat:b4ztp55qnfevbbt2swovrp7x5u

Flow-based Anomaly Detection Using Access Behavior Profiling and Time-sequenced Relation Mining

2016 KSII Transactions on Internet and Information Systems  
Using the behavior profiles and relation rules, this approach is able to detect possible anomalies and violations in real-time detection.  ...  In this paper, we propose an autonomous anomaly detection system based on behavior profiling and relation mining.  ...  Acknowledgements The authors would like to thank the anonymous reviewers for their detailed reviews and constructive comments, which help improve the quality of this paper.  ... 
doi:10.3837/tiis.2016.06.018 fatcat:atqie5h2prginfzxt35miczyna

Detecting localized homogeneous anomalies over spatio-temporal data

Aditya Telang, P. Deepak, Salil Joshi, Prasad Deshpande, Ranjana Rajendran
2014 Data mining and knowledge discovery  
Our approach differs significantly from traditional methods of spatial outlier detection, and employs two phases -i) discovering homogeneous regions, and ii) evaluating these regions as anomalies based  ...  Given the scale and richness of such data, finding spatio-temporal patterns that demonstrate significantly different behavior from their neighbors could be of interest for various application scenarios  ...  We presume it is much easier to visualize and understand a spatio-temporal anomaly when it is represented as a spatial anomaly that spans for a given time interval, rather than one that shrinks and grows  ... 
doi:10.1007/s10618-014-0366-x fatcat:njpa2odbwngdfga3z45qprioam

Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy [article]

Jiehui Xu, Haixu Wu, Jianmin Wang, Mingsheng Long
2022 arXiv   pre-print
Unsupervised detection of anomaly points in time series is a challenging problem, which requires the model to derive a distinguishable criterion.  ...  The Anomaly Transformer achieves state-of-the-art results on six unsupervised time series anomaly detection benchmarks of three applications: service monitoring, space earth exploration, and water treatment  ...  modeling and the reconstruction criterion for anomaly detection.  ... 
arXiv:2110.02642v4 fatcat:x5lmnu4nmjgm3llyvqbr53sh7y
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