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* [[Hidden Markov model]]s (HMMs)<ref name="replicator" />
* [[Hidden Markov model]]s (HMMs)<ref name="replicator" />
* [[Minimum Covariance Determinant]]<ref>{{Cite journal|last1=Hubert|first1=Mia|author-link=Mia Hubert|last2=Debruyne|first2=Michiel|last3=Rousseeuw|first3=Peter J.|author-link3=Peter J. Rousseeuw|date=2018|title=Minimum covariance determinant and extensions|journal=WIREs Computational Statistics|language=en|volume=10|issue=3|doi=10.1002/wics.1421|s2cid=67227041 |issn=1939-5108|doi-access=free}}</ref><ref>{{Cite journal|last1=Hubert|first1=Mia|author-link=Mia Hubert|last2=Debruyne|first2=Michiel|date=2010|title=Minimum covariance determinant|url=https://onlinelibrary.wiley.com/doi/abs/10.1002/wics.61|journal=WIREs Computational Statistics|language=en|volume=2|issue=1|pages=36–43|doi=10.1002/wics.61|s2cid=123086172 |issn=1939-0068}}</ref>
* [[Minimum Covariance Determinant]]<ref>{{Cite journal|last1=Hubert|first1=Mia|author-link=Mia Hubert|last2=Debruyne|first2=Michiel|last3=Rousseeuw|first3=Peter J.|author-link3=Peter J. Rousseeuw|date=2018|title=Minimum covariance determinant and extensions|journal=WIREs Computational Statistics|language=en|volume=10|issue=3|doi=10.1002/wics.1421|s2cid=67227041 |issn=1939-5108|doi-access=free}}</ref><ref>{{Cite journal|last1=Hubert|first1=Mia|author-link=Mia Hubert|last2=Debruyne|first2=Michiel|date=2010|title=Minimum covariance determinant|url=https://onlinelibrary.wiley.com/doi/abs/10.1002/wics.61|journal=WIREs Computational Statistics|language=en|volume=2|issue=1|pages=36–43|doi=10.1002/wics.61|s2cid=123086172 |issn=1939-0068}}</ref>
* [[Deep learning|Deep Learning]]<ref name=":32">{{Cite journal |date=2023-06-01 |title=Video anomaly detection system using deep convolutional and recurrent models |url=https://www.sciencedirect.com/science/article/pii/S2590123023001536 |journal=Results in Engineering |language=en-US |volume=18 |pages=101026 |doi=10.1016/j.rineng.2023.101026 |issn=2590-1230}}</ref>
** '''[[Convolutional neural network|Convolutional Neural Networks]] (CNNs):''' CNNs have shown exceptional performance in the unsupervised learning domain for anomaly detection, especially in image and video data analysis.<ref name=":32" />Their ability to automatically and hierarchically learn spatial hierarchies of features from low to high-level patterns makes them particularly suited for detecting visual anomalies. For instance, CNNs can be trained on image datasets to identify atypical patterns indicative of defects or out-of-norm conditions in industrial quality control scenarios.<ref>{{Cite journal |last=Alzubaidi |first=Laith |last2=Zhang |first2=Jinglan |last3=Humaidi |first3=Amjad J. |last4=Al-Dujaili |first4=Ayad |last5=Duan |first5=Ye |last6=Al-Shamma |first6=Omran |last7=Santamaría |first7=J. |last8=Fadhel |first8=Mohammed A. |last9=Al-Amidie |first9=Muthana |last10=Farhan |first10=Laith |date=2021-03-31 |title=Review of deep learning: concepts, CNN architectures, challenges, applications, future directions |url=https://doi.org/10.1186/s40537-021-00444-8 |journal=Journal of Big Data |volume=8 |issue=1 |pages=53 |doi=10.1186/s40537-021-00444-8 |issn=2196-1115 |pmc=PMC8010506 |pmid=33816053}}</ref>
** '''Simple Recurrent Units (SRUs):''' In time-series data, SRUs, a type of recurrent neural network, have been effectively used for anomaly detection by capturing temporal dependencies and sequence anomalies.<ref name=":32" />Unlike traditional RNNs, SRUs are designed to be faster and more parallelizable, offering a better fit for real-time anomaly detection in complex systems such as dynamic financial markets or predictive maintenance in machinery, where identifying temporal irregularities promptly is crucial.<ref>{{Cite journal |last=Belay |first=Mohammed Ayalew |last2=Blakseth |first2=Sindre Stenen |last3=Rasheed |first3=Adil |last4=Salvo Rossi |first4=Pierluigi |date=2023-01 |title=Unsupervised Anomaly Detection for IoT-Based Multivariate Time Series: Existing Solutions, Performance Analysis and Future Directions |url=https://www.mdpi.com/1424-8220/23/5/2844 |journal=Sensors |language=en |volume=23 |issue=5 |pages=2844 |doi=10.3390/s23052844 |issn=1424-8220}}</ref>


=== Cluster based ===
=== Cluster based ===

Revision as of 17:03, 8 November 2023

For broader coverage of this topic, see Outlier.
Part of a series on
Machine learning
and data mining
Paradigms
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Clustering
Dimensionality reduction
Structured prediction
Anomaly detection
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Reinforcement learning
Learning with humans
Model diagnostics
Mathematical foundations
Journals and conferences
Related articles

In data analysis, anomaly detection (also referred to as outlier detection and sometimes as novelty detection) is generally understood to be the identification of rare items, events or observations which deviate significantly from the majority of the data and do not conform to a well defined notion of normal behaviour.[1] Such examples may arouse suspicions of being generated by a different mechanism,[2] or appear inconsistent with the remainder of that set of data.[3]

Anomaly detection finds application in many domains including cyber security, medicine, machine vision, statistics, neuroscience, law enforcement and financial fraud to name only a few. Anomalies were initially searched for clear rejection or omission from the data to aid statistical analysis, for example to compute the mean or standard deviation. They were also removed to better predictions from models such as linear regression, and more recently their removal aids the performance of machine learning algorithms. However, in many applications anomalies themselves are of interest and are the observations most desirous in the entire data set, which need to be identified and separated from noise or irrelevant outliers.

Three broad categories of anomaly detection techniques exist.[1] Supervised anomaly detection techniques require a data set that has been labeled as "normal" and "abnormal" and involves training a classifier. However, this approach is rarely used in anomaly detection due to the general unavailability of labelled data and the inherent unbalanced nature of the classes. Semi-supervised anomaly detection techniques assume that some portion of the data is labelled. This may be any combination of the normal or anomalous data, but more often than not the techniques construct a model representing normal behavior from a given normal training data set, and then test the likelihood of a test instance to be generated by the model. Unsupervised anomaly detection techniques assume the data is unlabelled and are by far the most commonly used due to their wider and relevant application.

Definition

Many attempts have been made in the statistical and computer science communities to define an anomaly. The most prevalent ones include the following, and can be categorised into three groups: those that are ambiguous, those that are specific to a method with pre-defined thresholds usually chosen empirically, and those that are formally defined:

Ill defined

  • An outlier is an observation which deviates so much from the other observations as to arouse suspicions that it was generated by a different mechanism.[2]
  • Anomalies are instances or collections of data that occur very rarely in the data set and whose features differ significantly from most of the data.
  • An outlier is an observation (or subset of observations) which appears to be inconsistent with the remainder of that set of data.[3]
  • An anomaly is a point or collection of points that is relatively distant from other points in multi-dimensional space of features.
  • Anomalies are patterns in data that do not conform to a well defined notion of normal behaviour.[1]

Specific

  • Let T be observations from a univariate Gaussian distribution and O a point from T. Then the z-score for O is greater than a pre-selected threshold if and only if O is an outlier.

Applications

Anomaly detection is applicable in a very large number and variety of domains, and is an important subarea of unsupervised machine learning. As such it has applications in cyber-security, intrusion detection, fraud detection, fault detection, system health monitoring, event detection in sensor networks, detecting ecosystem disturbances, defect detection in images using machine vision, medical diagnosis and law enforcement.[4]

Intrusion detection

Anomaly detection was proposed for intrusion detection systems (IDS) by Dorothy Denning in 1986.[5] Anomaly detection for IDS is normally accomplished with thresholds and statistics, but can also be done with soft computing, and inductive learning.[6] Types of features proposed by 1999 included profiles of users, workstations, networks, remote hosts, groups of users, and programs based on frequencies, means, variances, covariances, and standard deviations.[7] The counterpart of anomaly detection in intrusion detection is misuse detection.

Preprocessing

Preprocessing data to remove anomalies can be an important step in data analysis, and is done for a number of reasons. Statistics such as the mean and standard deviation are more accurate after the removal of anomalies, and the visualisation of data can also be improved. In supervised learning, removing the anomalous data from the dataset often results in a statistically significant increase in accuracy.[8][9]

Video Surveillance

Anomaly detection has become increasingly vital in video surveillance to enhance security and safety.[10] [11]With the advent of deep learning technologies, methods using Convolutional Neural Networks (CNNs) and Simple Recurrent Units (SRUs) have shown significant promise in identifying unusual activities or behaviors in video data.[10] These models can process and analyze extensive video feeds in real-time, recognizing patterns that deviate from the norm, which may indicate potential security threats or safety violations.[10]

Many anomaly detection techniques have been proposed in literature.[1][12] The performance of methods usually depend on the data sets. For example, some may be suited to detecting local outliers, while others global, and methods have little systematic advantages over another when compared across many data sets.[13][14] Almost all algorithms also require the setting of non-intuitive parameters critical for performance, and usually unknown before application. Some of the popular techniques are mentioned below and are broken down into categories:

Statistical

Parameter-free

Parametric-based

Density

Neural networks

  • Replicator neural networks,[27] autoencoders, variational autoencoders,[28] long short-term memory neural networks[29]
  • Bayesian networks[27]
  • Hidden Markov models (HMMs)[27]
  • Minimum Covariance Determinant[30][31]
  • Deep Learning[32]
    • Convolutional Neural Networks (CNNs): CNNs have shown exceptional performance in the unsupervised learning domain for anomaly detection, especially in image and video data analysis.[32]Their ability to automatically and hierarchically learn spatial hierarchies of features from low to high-level patterns makes them particularly suited for detecting visual anomalies. For instance, CNNs can be trained on image datasets to identify atypical patterns indicative of defects or out-of-norm conditions in industrial quality control scenarios.[33]
    • Simple Recurrent Units (SRUs): In time-series data, SRUs, a type of recurrent neural network, have been effectively used for anomaly detection by capturing temporal dependencies and sequence anomalies.[32]Unlike traditional RNNs, SRUs are designed to be faster and more parallelizable, offering a better fit for real-time anomaly detection in complex systems such as dynamic financial markets or predictive maintenance in machinery, where identifying temporal irregularities promptly is crucial.[34]

Cluster based

Ensembles

Others

Explainable Anomaly Detection

Many of the methods discussed above only yield an anomaly score prediction, which often can be explained to users as the point being in a region of low data density (or relatively low density compared to the neighbor's densities). In explainable artificial intelligence, the users demand methods with higher explainability. Some methods allow for more detailed explanations:

  • The Subspace Outlier Degree (SOD)[22] identifies attributes where a sample is normal, and attributes in which the sample deviates from the expected.
  • Correlation Outlier Probabilities (COP)[23] compute an error vector how a sample point deviates from an expected location, which can be interpreted as a counterfactual explanation: the sample would be normal if it were moved to that location.

Software

  • ELKI is an open-source Java data mining toolkit that contains several anomaly detection algorithms, as well as index acceleration for them.
  • PyOD is an open-source Python library developed specifically for anomaly detection.[43]
  • scikit-learn is an open-source Python library that contains some algorithms for unsupervised anomaly detection.
  • Wolfram Mathematica provides functionality for unsupervised anomaly detection across multiple data types [44]

Datasets

See also

References

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  2. ^ a b Hawkins, Douglas M. (1980). Identification of Outliers. Chapman and Hall London; New York.
  3. ^ a b Barnett, Vic; Lewis, Lewis (1978). Outliers in statistical data. John Wiley & Sons Ltd.
  4. ^ Aggarwal, Charu (2017). Outlier Analysis. Springer Publishing Company, Incorporated. ISBN 978-3319475776.
  5. ^ Denning, D. E. (1987). "An Intrusion-Detection Model" (PDF). IEEE Transactions on Software Engineering. SE-13 (2): 222–232. CiteSeerX 10.1.1.102.5127. doi:10.1109/TSE.1987.232894. S2CID 10028835. Archived (PDF) from the original on June 22, 2015.
  6. ^ Teng, H. S.; Chen, K.; Lu, S. C. (1990). "Adaptive real-time anomaly detection using inductively generated sequential patterns". Proceedings. 1990 IEEE Computer Society Symposium on Research in Security and Privacy (PDF). pp. 278–284. doi:10.1109/RISP.1990.63857. ISBN 978-0-8186-2060-7. S2CID 35632142.
  7. ^ Jones, Anita K.; Sielken, Robert S. (1999). "Computer System Intrusion Detection: A Survey". Technical Report, Department of Computer Science, University of Virginia, Charlottesville, VA. CiteSeerX 10.1.1.24.7802.
  8. ^ Tomek, Ivan (1976). "An Experiment with the Edited Nearest-Neighbor Rule". IEEE Transactions on Systems, Man, and Cybernetics. 6 (6): 448–452. doi:10.1109/TSMC.1976.4309523.
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  33. ^ Alzubaidi, Laith; Zhang, Jinglan; Humaidi, Amjad J.; Al-Dujaili, Ayad; Duan, Ye; Al-Shamma, Omran; Santamaría, J.; Fadhel, Mohammed A.; Al-Amidie, Muthana; Farhan, Laith (2021-03-31). "Review of deep learning: concepts, CNN architectures, challenges, applications, future directions". Journal of Big Data. 8 (1): 53. doi:10.1186/s40537-021-00444-8. ISSN 2196-1115. PMC 8010506. PMID 33816053.{{cite journal}}: CS1 maint: PMC format (link) CS1 maint: unflagged free DOI (link)
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  44. ^ [1] Mathematica documentation
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