A comprehensive survey of numeric and symbolic outlier mining techniques

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Mining association rules between sets of items in large databases

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Exploratory Data Analysis

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Robust Regression and Outlier Detection

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LOF: identifying density-based local outliers

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Mining association rules between sets of items in large databases

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BIRCH: an efficient data clustering method for very large databases

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A Survey of Outlier Detection Methodologies

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Identification of Outliers

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Outliers in Statistical Data

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Anomaly detection: A survey

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A review of novelty detection

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The application of data mining techniques in financial fraud detection: a classification framework and an academic review of literature

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Network Anomaly Detection: Methods, Systems and Tools

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Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection

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A survey on unsupervised outlier detection in high‐dimensional numerical data

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Video-Based Abnormal Human Behavior Recognition—A Review

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Anomaly detection in dynamic networks: a survey

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Financial fraud detection applying data mining techniques: A comprehensive review from 2009 to 2019

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Review and big data perspectives on robust data mining approaches for industrial process modeling with outliers and missing data

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Local outlier detection reconsidered: a generalized view on locality with applications to spatial, video, and network outlier detection

Schubert, E., Zimek, A., & Kriegel, H. (2012) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-012-0300-z · Cited by 314

Real-Time Deep Anomaly Detection Framework for Multivariate Time-Series Data in Industrial IoT

Nizam, H., Zafar, S., Lv, Z., Wang, F., & Hu, X. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3211874 · Cited by 247

Outlier Detection: Applications And Techniques

Singh, K., & Upadhyaya, S. (2012) · Cited by 212

Distributed Online One-Class Support Vector Machine for Anomaly Detection Over Networks

Miao, X., Liu, Y., Zhao, H., & Li, C. (2018) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2018.2804940 · Cited by 192

There and back again: Outlier detection between statistical reasoning and data mining algorithms

Zimek, A., & Peter Filzmoser, A. (2018) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1280 · Cited by 201

A Review of Financial Accounting Fraud Detection based on Data Mining Techniques

Sharma, A., & Panigrahi, P. K. (2012) · International Journal of Computer Applications · doi:10.5120/4787-7016 · Cited by 183

Network Anomaly Detection: A Machine Learning Perspective

Bhattacharyya, D. K., & Kalita, J. K. (2013) · Cited by 150

A Survey on Explainable Anomaly Detection

Li, Z., Zhu, Y., & Van Leeuwen, M. (2024) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3609333 · Cited by 139

Algorithmic Reason

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Self-supervised anomaly detection in computer vision and beyond: A survey and outlook

Hojjati, H., Ho, T. K. K., & Armanfard, N. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106106 · Cited by 149

Governing others: Anomaly and the algorithmic subject of security

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Anomaly Detection Methods for Categorical Data

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A Survey on Anomaly detection in Evolving Data

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Value-added tax fraud detection with scalable anomaly detection techniques

Vanhoeyveld, J., Martens, D., & Peeters, B. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.105895 · Cited by 98

Anomaly Detection in Smart Environments: A Comprehensive Survey

Fährmann, D., Martín, L., Sánchez, L., & Damer, N. (2024) · IEEE Access · doi:10.1109/access.2024.3395051 · Cited by 89

Mining social networks for anomalies: Methods and challenges

Bindu, P. V., & Thilagam, P. S. (2016) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2016.02.021 · Cited by 80

Enhancing Fault Classification Accuracy of Ball Bearing Using Central Tendency Based Time Domain Features

Tahir, M. M., Khan, A. Q., Iqbal, N., Hussain, A., & Badshah, S. (2016) · IEEE Access · doi:10.1109/access.2016.2608505 · Cited by 75

Robust one-class SVM for fault detection

Xiao, Y., Wang, H., Xu, W., & Zhou, J. (2015) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2015.11.010 · Cited by 72

Anomaly detection on the edge

Schneible, J., & Lu, A. (2017) · IEEE Military Communications Conference (MILCOM) · doi:10.1109/milcom.2017.8170817 · Cited by 70

Hunting for Insider Threats Using LSTM-Based Anomaly Detection

Villarreal-Vasquez, M., Modelo-Howard, G., Dube, S., & Bhargava, B. (2023) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2021.3135639 · Cited by 52

Domain Anomaly Detection in Machine Perception: A System Architecture and Taxonomy

Kittler, J., Christmas, W. J., de Campos, T. E., Windridge, D., Yan, F., Illingworth, J., & Osman, M. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.209 · Cited by 53

A survey on Neyman‐Pearson classification and suggestions for future research

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Comparison of novelty detection methods for multispectral images in rover-based planetary exploration missions

Kerner, H., Wagstaff, K. L., Bue, B., Wellington, D., Jacob, S., Horton, P., Bell, J. F., III, Kwan, C., & Amor, H. B. (2020) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-020-00697-6 · Cited by 45

Anomaly detection for symbolic sequences and time series data

Kumar, V., Banerjee, A., & Chandola, V. (2009) · Cited by 47

Anomaly detection

SCHNEIDER, P. L., & Xhafa, F. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-823818-9.00013-4 · Cited by 43

Anomaly Detection for Urban Vehicle GNSS Observation with a Hybrid Machine Learning System

Xia, Y., Pan, S., Meng, X., Gao, W., Ye, F., Zhao, Q. L., & Zhao, X. (2020) · Remote Sensing · doi:10.3390/rs12060971 · Cited by 39

Impact of Outlier Detection on Neural Networks Based Property Value Prediction

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Data-driven approaches for impending fault detection of industrial systems: a review

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Online anomaly detection on the webscope S5 dataset: A comparative study

Thill, M., Konen, W., & Bäck, T. (2017) · IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) · doi:10.1109/eais.2017.7954844 · Cited by 37

A plug-in approach to neyman-pearson classification

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Development of an intelligent information system for financial analysis depend on supervised machine learning algorithms

Lei, X., Mohamad, U. H., Sarlan, A. B., Shutaywi, M., Daradkeh, Y. I., & Mohammed, H. O. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.103036 · Cited by 34

Meta-survey on outlier and anomaly detection

Olteanu, M., Fabrice Rossi, A., & Yger, F. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126634 · Cited by 44

Customs fraud detection

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Inter-image outliers and their application to image classification

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Automated novelty detection in the WISE survey with one-class support vector machines

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Machine learning for detection of anomalies in press-hardening: Selection of efficient methods

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Unsupervised Anomaly Detection for High Dimensional Data—an Exploratory Analysis

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A spatial filtering inspired three-way clustering approach with application to outlier detection

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SDCOR: Scalable density-based clustering for local outlier detection in massive-scale datasets

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Non-Technical Loss Detection in Power Grids with Statistical Profile Images Based on Semi-Supervised Learning

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A Survey on Financial Fraud Detection Methodologies

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Ensemble-learning based neural networks for novelty detection in multi-class systems

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Automated novelty detection in the WISE survey with one-class support vector machines

Solarz, A., Bilicki, M., Gromadzki, M., Pollo, A., Durkalec, A., & Wypych, M. (2017) · Springer Link (Chiba Institute of Technology) · doi:10.1051/0004-6361/201730968/pdf · Cited by 29

Log Analysis Techniques using Clustering in Network ForensicsPreprint

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Structural Attacks and Defenses for Flow-Based Microfluidic Biochips

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Novelty Detection as an Intrinsic Motivation for Cumulative Learning Robots

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Finding Fraud in Health Insurance Data with Two-Layer Outlier Detection Approach

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A Framework for Discovering Internal Financial Fraud Using Analytics

Panigrahi, P. K. (2011) · International Conference on Communication Systems and Network Technologies · doi:10.1109/csnt.2011.74 · Cited by 19

Palisade: A framework for anomaly detection in embedded systems

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A Multi-Module Machine Learning Approach to Detect Tax Fraud

Alsadhan, N. A. (2023) · Computer Systems Science and Engineering · doi:10.32604/csse.2023.033375 · Cited by 18

Mining multidimensional contextual outliers from categorical relational data

Tang, G., Bailey, J., Pei, J., & Dong, G. (2013) · International Conference on Scientific and Statistical Database Management · doi:10.1145/2484838.2484883 · Cited by 18

Maritime anomaly detection in ferry tracks

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Privacy-Preserving Anomaly Detection Using Synthetic Data

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Effortless Data Exploration with zenvisage: An Expressive and Interactive Visual Analytics SystemPreprint

Siddiqui, T., Kim, A., Lee, J., Karahalios, K. G., & Parameswaran, A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1604.03583 · Cited by 17

Outlier detection in multivariate functional data through a contaminated mixture model

Amovin-Assagba, M., Gannaz, I., & Jacques, J. (2022) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2022.107496 · Cited by 16

Combination of data mining techniques for intrusion detection system

Elekar, K. (2015) · International Conference on Computer, Communication and Control · doi:10.1109/ic4.2015.7375727 · Cited by 16

Adaptive rough radial basis function neural network with prototype outlier removal

Goh, P. Y., Tan, S. C., Cheah, W. P., & Lim, C. P. (2019) · Information Sciences · doi:10.1016/j.ins.2019.07.066 · Cited by 16

A Bayesian forecasting and anomaly detection framework for vehicular monitoring networks

Scalabrin, M., Gadaleta, M., Bonetto, R., & Rossi, M. (2017) · Machine learning for signal processing ... · doi:10.1109/mlsp.2017.8168151 · Cited by 15

Outlier preservation by dimensionality reduction techniques

Onderwater, M. (2015) · International Journal of Data Analysis Techniques and Strategies · doi:10.1504/ijdats.2015.071365 · Cited by 14

Using Noisy Binary Search for Differentially Private Anomaly Detection

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A Comparative Study of Outlier Detection Algorithms

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Detecting unusual user proles with outlier detection techniques

Onderwater, M. (2010) · Cited by 13

Online anomaly detection using non-parametric technique for big data streams in cloud collaborative environment

Nair, S. G. S., Munirathinam, S., & Balakrishnan, R. (2016) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2016.7840816 · Cited by 13

Sparse representation and overcomplete dictionary learning for anomaly detection in electrocardiograms

Andrysiak, T. (2018) · Neural Computing and Applications · doi:10.1007/s00521-018-3814-5 · Cited by 13

Predicting anomaly conditions of energy equipment using neural networks

Sverdlova, A. D., & Zaporozhets, A. O. (2021) · E3S Web of Conferences · doi:10.1051/e3sconf/202128009005 · Cited by 11

A Mathematical Approach for Mining Web Content Outliers using Term Frequency Ranking

Bama, S. S., Ahmed, M., & Saravanan, A. (2015) · Indian Journal of Science and Technology · doi:10.17485/ijst/2015/v8i14/55679 · Cited by 10

A survey on outliers detection in distributed data mining for big data

Ajitha, P., & Chandra, E. (2020) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.4304599 · Cited by 9

Development of a method for fraud detection in heterogeneous data during installation of mobile applications

Polhul, T., & Yarovyi, A. A. (2019) · Eastern-European Journal of Enterprise Technologies · doi:10.15587/1729-4061.2019.155060 · Cited by 9

Log‐based anomaly detection for distributed systems: State of the art, industry experience, and open issues

Wei, X., Wang, J., Sun, C., Towey, D., Shoufeng, Z., Zuo, W., Yu, Y., Ruan, R., & Song, G. (2024) · Journal of Software Evolution and Process · doi:10.1002/smr.2650 · Cited by 14

Meta-analysis of fraud, waste and abuse detection methods in healthcare

Ikono, R. N., Iroju, O. A., Janet, O., & Oyegoke, T. O. (2019) · Nigerian Journal of Technology · doi:10.4314/njt.v38i2.28 · Cited by 9

A fast noise resilient anomaly detection using GMM-based collective labelling

Bigdeli, E., Raahemi, B., Mohammadi, M., & Matwin, S. (2015) · Science and Information Conference · doi:10.1109/sai.2015.7237166 · Cited by 9

Anomaly Detection for Application Log Data

Grover, A. (2018) · doi:10.31979/etd.znsb-bw4d · Cited by 9

A New Validity Detection Method of Online Status Monitoring Data for Power Transformer

Zhou, H., Lu, L., Wang, G., & Su, Z. (2024) · IEEE Access · doi:10.1109/access.2024.3358399 · Cited by 8

Context-Aware Deep Learning-Driven Framework for Mitigation of Security Risks in BYOD-Enabled Environments

Petrov, D., & Znati, T. F. (2018) · IEEE International Conference on Collaboration and Internet Computing (CIC) · doi:10.1109/cic.2018.00032 · Cited by 8

XML-AD: Detecting anomalous patterns in XML documents

Menahem, E., Schclar, A., Rokach, L., & Elovici, Y. (2015) · Information Sciences · doi:10.1016/j.ins.2015.07.007 · Cited by 8

PREMIuM: Big data platform for enabling self-healing manufacturing

Stojanović, L., & Stojanović, N. (2017) · IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC) · doi:10.1109/ice.2017.8280060 · Cited by 8

Anomaly detection and prediction of sensors faults in a refinery using data mining techniques and fuzzy logic

Saybani, M. R. (2011) · Scientific Research and Essays · doi:10.5897/sre11.333 · Cited by 7

Noting Anomalies in Streams of Symbolic Predicates Using A-Distance

Cox, M. T., Oates, T., Paisner, M., & Perlis, D. (2013) · Cited by 7

Should I Raise The Red Flag? A comprehensive survey of anomaly scoring methods toward mitigating false alarmsPreprint

Zohrevand, Z., & Glässer, U. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.06646 · Cited by 15

Anomaly Detection Method for Online Discussion

Krammer, P. H., Habala, O., Mojžiš, J., Hluchý, L., & Jurkovič, M. (2019) · Procedia Computer Science · doi:10.1016/j.procs.2019.08.045 · Cited by 6

Resistant learning on the envelope bulk for identifying anomalous patterns

Huang, S., Yu, F., Tsaih, R., & Huang, Y. (2014) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2014.6889485 · Cited by 6

An FPGA-based spectral anomaly detection system

Moss, D. J. M., Zhang, Z., Fraser, N. J., & Leong, P. H. W. (2014) · International Conference on Field-Programmable Technology (FPT) · doi:10.1109/fpt.2014.7082772 · Cited by 6

Graph-based Anomaly Detection

(2013) · Chapman & Hall/CRC eBooks · doi:10.1201/b15352-15 · Cited by 6

zenvisage: Effortless Visual Data Exploration.Preprint

Siddiqui, T., Kim, A., Lee, J. R., Karahalios, K. G., & Parameswaran, A. (2016) · arXiv (Cornell University) · Cited by 6

A Statistical Technique for Online Anomaly Detection for Big Data Streams in Cloud Collaborative Environment

Nair, S. G. S., & Balakrishnan, R. (2016) · IEEE International Conference on Computer and Information Technology (CIT) · doi:10.1109/cit.2016.80 · Cited by 5

Multi-level relationship outlier detection

Jiang, Q., Campbell, A., Tang, G., & Pei, J. (2012) · International Journal of Business Intelligence and Data Mining · doi:10.1504/ijbidm.2012.051713 · Cited by 5

Alignment and clustering of breast cancer patients by longitudinal treatment history.

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An EffectiveMulti-Layer Model for Controlling the Quality of Data

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Securing Your Transactions: Detecting Anomalous Patterns In XML DocumentsPreprint

Menahem, E., Schclar, A., Rokach, L., & Elovici, Y. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1209.1797 · Cited by 5

A novel algorithm for enhancing search results by detecting dissimilar patterns based on correlation method.

Poonkuzhali, S., Ravi, K. K., & Shanmugam, T. (2017) · The International Arab Journal of Information Technology · Cited by 4

Data mining for outliers with robust statistics

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Detection of outliers : a data mining approach

Fiott, T. (2015)

A SURVEY OF OUTLIER DETECTION IN DATA MINING

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Outliers and data mining : finding exceptions in data

Knorr, E. M. (2009) · Open Collections · doi:10.14288/1.0051497 · Cited by 20

Noname manuscript No. (will be inserted by the editor) Mining Outlying Aspects on Numeric Data

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