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Approach in data analysis
In data analysis, anomaly detection (also referred to as outlier detection and sometimes as novelty detection) is generally understood to be the identification
Anomaly_detection
Network protection device or software
detection approach. The most well-known variants are signature-based detection (recognizing bad patterns, such as exploitation attempts) and anomaly-based
Intrusion_detection_system
Algorithm for anomaly detection
is an unsupervised learning algorithm for anomaly detection that works on the principle of isolating anomalies, instead of the most common techniques of
Isolation_forest
Instrument for detecting variations in the Earth's magnetic field
Underwater Detection and Tracking Systems". Chengjing Li; et al. (2015). "Detection Range of Airborne Magnetometers in Magnetic Anomaly Detection". Journal
Magnetic_anomaly_detector
Subset of artificial intelligence
Three broad categories of anomaly detection techniques exist. Unsupervised anomaly detection techniques detect anomalies in an unlabelled test data set
Machine_learning
Approach to network security
Network behavior anomaly detection (NBAD) is a security technique that provides network security threat detection. It is a complementary technology to
Network behavior anomaly detection
Network_behavior_anomaly_detection
Field of computer security
visibility and anomaly detection could help detect zero-days or polymorphic code. Primarily due to low rates of anti-virus detection against this type
Security information and event management
Security_information_and_event_management
An anomaly-based intrusion detection system, is an intrusion detection system for detecting both network and computer intrusions and misuse by monitoring
Anomaly-based intrusion detection system
Anomaly-based_intrusion_detection_system
Neural network that learns efficient data encoding in an unsupervised manner
applied to many problems, including facial recognition, feature detection, anomaly detection, and learning the meaning of words. In terms of data synthesis
Autoencoder
Statistics and machine learning technique
unsupervised learning scenarios, for example in consensus clustering or in anomaly detection. Empirically, ensembles tend to yield better results when there is
Ensemble_learning
Observation far apart from others in statistics and data science
econometrics, manufacturing, networking and data mining, the task of anomaly detection may take other approaches. Some of these may be distance-based and
Outlier
Interdisciplinary field of study
that are further used for making Classifications, Predictions, and Anomaly detections by advanced Statistical approaches, digital image processing and machine
Astroinformatics
Type of feedforward neural network
Xiaoyu; Xing, Tony; Yang, Mao; Tong, Jie; Zhang, Qi (2019). Time-Series Anomaly Detection Service at Microsoft | Proceedings of the 25th ACM SIGKDD International
Convolutional_neural_network
Process of analyzing large data sets
such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining, sequential pattern mining)
Data_mining
Practice of subverting video game rules or mechanics to gain an unfair advantage
Reports can include data such as screenshots, videos, and chatlogs. Anomalies in player behavior can be detected by statistically analyzing game events
Cheating_in_online_games
Real-time observability platform
integrations for metrics collection, log management, machine learning-based anomaly detection, and AI-assisted troubleshooting. The company behind Netdata also
Netdata
Paradigm in machine learning that uses no classification labels
algorithms used in unsupervised learning include: (1) Clustering, (2) Anomaly detection, (3) Approaches for learning latent variable models. Each approach
Unsupervised_learning
Algorithm for anomaly detection
In anomaly detection, the local outlier factor (LOF) is an algorithm proposed by Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng and Jörg Sander
Local_outlier_factor
Type of machine learning model
Pairs), Stereo Set, and Parity Benchmark. Fact-checking and misinformation detection benchmarks are available. A 2023 study compared the fact-checking accuracy
Large_language_model
Model of differential privacy
subsequent analyses, such as anomaly detection. Anomaly detection on the proposed method's reconstructed data achieves a detection accuracy similar to that
Local_differential_privacy
Recurrent neural network architecture
language translation Protein homology detection Predicting subcellular localization of proteins Time series anomaly detection Several prediction tasks in the
Long_short-term_memory
Difficulties arising when analyzing data with many aspects ("dimensions")
survey, Zimek et al. identified the following problems when searching for anomalies in high-dimensional data: Concentration of scores and distances: derived
Curse_of_dimensionality
Statistical analysis
generally change detection also includes the detection of anomalous behavior: anomaly detection. In offline change point detection it is assumed that
Change_detection
Type of large language model
Regression Clustering Dimensionality reduction Density estimation Anomaly detection Data cleaning AutoML Association rules Semantic analysis Structured
Generative pre-trained transformer
Generative_pre-trained_transformer
Class of artificial neural network
recognition Speech synthesis Brain–computer interfaces Time series anomaly detection Text-to-Video model Energy forecasting Rhythm learning Music composition
Recurrent_neural_network
American Internet measurement company
Kentik is an American network observability, network monitoring and anomaly detection company headquartered in San Francisco, California. Kentik was founded
Kentik
Weapon to be used in anti-submarine warfare
anti-submarine forces also began employing autogyro aircraft and Magnetic Anomaly Detection (MAD) equipment to sink U.S. subs, particularly those plying major
Anti-submarine_weapon
Type of database that uses vectors to represent other data
semantic search, multi-modal search, recommendations engines, object detection, and retrieval-augmented generation (RAG). Vector embeddings are mathematical
Vector_database
Overview of and topical guide to machine learning
clustering k-means clustering k-medians Mean-shift OPTICS algorithm Anomaly detection k-nearest neighbors algorithm (k-NN) Local outlier factor Semi-supervised
Outline_of_machine_learning
"Maritime anomaly detection" returns a large number of hits from a wide range of domains. Some of the related topics are: Maritime anomaly detection and situation
Global Maritime Situational Awareness
Global_Maritime_Situational_Awareness
Class of artificial neural networks
graph, a network of computers can be analyzed with GNNs for anomaly detection. Anomalies within provenance graphs often correlate to malicious activity
Graph_neural_network
2023 text-generating language model
Regression Clustering Dimensionality reduction Density estimation Anomaly detection Data cleaning AutoML Association rules Semantic analysis Structured
GPT-4
Algorithm for modelling sequential data
Regression Clustering Dimensionality reduction Density estimation Anomaly detection Data cleaning AutoML Association rules Semantic analysis Structured
Transformer_(deep_learning)
Reverse-engineering neural networks
Regression Clustering Dimensionality reduction Density estimation Anomaly detection Data cleaning AutoML Association rules Semantic analysis Structured
Mechanistic_interpretability
Machine learning model for vision processing
such as in image classification, object detection, video deepfake detection, image segmentation, anomaly detection, image synthesis, cluster analysis, autonomous
Vision_transformer
Machine learning methods using multiple input modalities
and patient records to improve diagnostic accuracy and early disease detection, especially in cancer screening. Content generation: models like DALL·E
Multimodal_learning
2026 text-generating language model
best-performing models by 4.2% on semantic similarity (or paraphrase detection), evaluating the ability to predict whether two sentences are paraphrases
GPT-1
Type of feedforward neural network
Regression Clustering Dimensionality reduction Density estimation Anomaly detection Data cleaning AutoML Association rules Semantic analysis Structured
Multilayer_perceptron
Machine learning technique
Regression Clustering Dimensionality reduction Density estimation Anomaly detection Data cleaning AutoML Association rules Semantic analysis Structured
Reinforcement learning from human feedback
Reinforcement_learning_from_human_feedback
Method used to normalize the range of independent variables
Regression Clustering Dimensionality reduction Density estimation Anomaly detection Data cleaning AutoML Association rules Semantic analysis Structured
Feature_scaling
Model-free reinforcement learning algorithm
Regression Clustering Dimensionality reduction Density estimation Anomaly detection Data cleaning AutoML Association rules Semantic analysis Structured
Proximal_policy_optimization
Artificial intelligence field of study
proportion that the model is correct. Similarly, anomaly detection or out-of-distribution (OOD) detection aims to identify when an AI system is in an unusual
AI_safety
American multinational technology corporation
Confluent Cloud providing AI-powered capabilities including anomaly detection, fraud detection, and forecasting for real-time data streams. "Confluent, the
Confluent
Machine learning calibration technique
Regression Clustering Dimensionality reduction Density estimation Anomaly detection Data cleaning AutoML Association rules Semantic analysis Structured
Platt_scaling
Artificial intelligence in IT operations
environments, aiming to automate processes such as event correlation, anomaly detection, and causality determination. AIOps refers to multi-layered, complex
AIOps
Origins and events of data
forensic activities such as data-dependency analysis, error/compromise detection, recovery, auditing and compliance analysis: "Lineage is a simple type
Data_lineage
Deep learning architecture
Regression Clustering Dimensionality reduction Density estimation Anomaly detection Data cleaning AutoML Association rules Semantic analysis Structured
Mamba (deep learning architecture)
Mamba_(deep_learning_architecture)
Anomaly Detection at Multiple Scales, or ADAMS was a $35 million DARPA project designed to identify patterns and anomalies in very large data sets. It
Anomaly Detection at Multiple Scales
Anomaly_Detection_at_Multiple_Scales
Conversational software
Regression Clustering Dimensionality reduction Density estimation Anomaly detection Data cleaning AutoML Association rules Semantic analysis Structured
Chatbot
Change of statistical properties over time
drifting damage. (2022) NAB: The Numenta Anomaly Benchmark, benchmark for evaluating algorithms for anomaly detection in streaming, real-time applications
Concept_drift
Computational model used in machine learning
networks are widely applied in cybersecurity for anomaly detection, malware classification, and intrusion detection. By learning patterns of normal system or
Neural network (machine learning)
Neural_network_(machine_learning)
Research field that lies at the intersection of machine learning and computer security
Kloft, M.; Laskov, P. (2012). "Security analysis of online centroid anomaly detection" (PDF). Journal of Machine Learning Research. 13: 3647–3690. Rao,
Adversarial_machine_learning
Machine-learning and computational-neuroscience conference
Regression Clustering Dimensionality reduction Density estimation Anomaly detection Data cleaning AutoML Association rules Semantic analysis Structured
Conference on Neural Information Processing Systems
Conference_on_Neural_Information_Processing_Systems
British computer scientist
the proof of concept platform using these methods for system state anomaly detection, from 2002 to the present, and received widespread use. Based on these
Mark Burgess (computer scientist)
Mark_Burgess_(computer_scientist)
2026 multimodal model by OpenAI
Regression Clustering Dimensionality reduction Density estimation Anomaly detection Data cleaning AutoML Association rules Semantic analysis Structured
GPT-5
Process of automating the application of machine learning
miscellaneous formats) Column type detection; e.g., Boolean, discrete numerical, continuous numerical, or text Column intent detection; e.g., target/label, stratification
Automated_machine_learning
Software user interface
Regression Clustering Dimensionality reduction Density estimation Anomaly detection Data cleaning AutoML Association rules Semantic analysis Structured
Human-in-the-loop
Smooth approximation of one-hot arg max
Regression Clustering Dimensionality reduction Density estimation Anomaly detection Data cleaning AutoML Association rules Semantic analysis Structured
Softmax_function
Vector quantization algorithm minimizing the sum of squared deviations
organization of the genome. In such analyses, clustering of similarity or co-detection matrices can reveal groups of genomic windows that exhibit coordinated
K-means_clustering
Facts provided or learned about something or someone
information retrieval, intelligence gathering, plagiarism detection, pattern recognition, anomaly detection and even art creation. Often information can be viewed
Information
Machine learning technique
Regression Clustering Dimensionality reduction Density estimation Anomaly detection Data cleaning AutoML Association rules Semantic analysis Structured
Transfer_learning
Technique for the generative modeling of a continuous probability distribution
Regression Clustering Dimensionality reduction Density estimation Anomaly detection Data cleaning AutoML Association rules Semantic analysis Structured
Diffusion_model
Scientific study of digital information
information retrieval, intelligence gathering, plagiarism detection, pattern recognition, anomaly detection, the analysis of music, art creation, imaging system
Information_theory
Subutai (12 October 2015). "Evaluating Real-Time Anomaly Detection Algorithms -- the Numenta Anomaly Benchmark". 2015 IEEE 14th International Conference
List of datasets for machine-learning research
List_of_datasets_for_machine-learning_research
Technique in machine learning
other domains: Natural language processing: Part-of-speech tagging Intent detection Sentiment analysis Machine translation Speech recognition Language model
Curriculum_learning
Topics referred to by the same term
Look up anomaly or anomalous in Wiktionary, the free dictionary. Anomaly, The Anomaly or Anomalies may refer to: Anomaly (natural sciences) Atmospheric
Anomaly
Deep learning method
adversarial network and texture features applied to automatic glaucoma detection". Applied Soft Computing. 90 106165. doi:10.1016/j.asoc.2020.106165. S2CID 214571484
Generative adversarial network
Generative_adversarial_network
Class of algorithms for pattern analysis
Regression Clustering Dimensionality reduction Density estimation Anomaly detection Data cleaning AutoML Association rules Semantic analysis Structured
Kernel_method
Software program
automated choices." Art portal Artificial imagination DALL-E Feature detection (computer vision) Hallucination (artificial intelligence) Neural style
DeepDream
Type of convolutional neural network
Yong-Cheng; Lin, Chun-Liang (2023-02-14). "Deep learning based atomic defect detection framework for two-dimensional materials". Scientific Data. 10 (1): 91
U-Net
AI platform developed by IBM
IBM Safer Payments, IBM watsonx has been used in banking sector fraud detection and anti-money laundering (AML) systems. Watsonx.ai is a platform that
IBM_Watsonx
Analysis of social structures using network and graph theory
core problems, such as influence blocking, community detection, centrality ranking, anomaly detection, and network anonymization..[citation needed] Visual
Social_network_analysis
3D reconstruction technique
Regression Clustering Dimensionality reduction Density estimation Anomaly detection Data cleaning AutoML Association rules Semantic analysis Structured
Neural_radiance_field
Automated recognition of patterns and regularities in data
authentication: e.g., license plate recognition, fingerprint analysis, face detection/verification, and voice-based authentication. medical diagnosis: e.g.
Pattern_recognition
Type of artificial intelligence system
Regression Clustering Dimensionality reduction Density estimation Anomaly detection Data cleaning AutoML Association rules Semantic analysis Structured
Vision-language_model
Field of machine learning
with fewer (or no) parameters under a large number of conditions bug detection in software projects continuous learning combinations with logic-based
Reinforcement_learning
Machine learning technique
Regression Clustering Dimensionality reduction Density estimation Anomaly detection Data cleaning AutoML Association rules Semantic analysis Structured
Mixture_of_experts
Conformance of AI to intended objectives
connections to interpretability research, (adversarial) robustness, anomaly detection, calibrated uncertainty, formal verification, preference learning
AI_alignment
Integrated circuit technology
Regression Clustering Dimensionality reduction Density estimation Anomaly detection Data cleaning AutoML Association rules Semantic analysis Structured
Neuromorphic_computing
Theory of machine learning
Regression Clustering Dimensionality reduction Density estimation Anomaly detection Data cleaning AutoML Association rules Semantic analysis Structured
Computational_learning_theory
Computer programming concept
Regression Clustering Dimensionality reduction Density estimation Anomaly detection Data cleaning AutoML Association rules Semantic analysis Structured
Temporal_difference_learning
Machine learning strategy
Alan; Emmott, Andrew (2016). "Incorporating Expert Feedback into Active Anomaly Discovery". In Bonchi, Francesco; Domingo-Ferrer, Josep; Baeza-Yates, Ricardo;
Active learning (machine learning)
Active_learning_(machine_learning)
Deep learning library
Regression Clustering Dimensionality reduction Density estimation Anomaly detection Data cleaning AutoML Association rules Semantic analysis Structured
PyTorch
Set of methods for supervised statistical learning
can be used for classification, regression, or other tasks like outlier detection. Intuitively, a good separation is achieved by the hyperplane that has
Support_vector_machine
Machine learning paradigm using minimal training data
Regression Clustering Dimensionality reduction Density estimation Anomaly detection Data cleaning AutoML Association rules Semantic analysis Structured
Few-shot_learning
Biological theory of intelligence
Jeff Hawkins with Sandra Blakeslee, HTM is primarily used today for anomaly detection in streaming data. The technology is based on neuroscience and the
Hierarchical_temporal_memory
Chinese-American computer scientist
computer scientist whose research interests include cybersecurity and anomaly detection as well as machine learning in digital health. She is a professor
Danfeng_Yao
Data that is useful in detecting or predicting cyberattacks
A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection". 2019 IEEE Conference on Communications and Network Security (CNS)
Cyber_threat_intelligence
Optimization algorithm
Regression Clustering Dimensionality reduction Density estimation Anomaly detection Data cleaning AutoML Association rules Semantic analysis Structured
Gradient_descent
Concept in machine learning
Claudia Perlich (January 2011). "Leakage in data mining: Formulation, detection, and avoidance". Proceedings of the 17th ACM SIGKDD international conference
Leakage_(machine_learning)
Memory unit used in neural networks
Regression Clustering Dimensionality reduction Density estimation Anomaly detection Data cleaning AutoML Association rules Semantic analysis Structured
Gated_recurrent_unit
Statistical model used in machine learning
generation Point-cloud modeling Video generation Lossy image compression Anomaly detection Tabak, Esteban G.; Vanden-Eijnden, Eric (2010). "Density estimation
Flow-based_generative_model
Method of data analysis
regression, in selecting a subset of variables from x, and in outlier detection. Property 3: (Spectral decomposition of Σ) Σ = λ 1 α 1 α 1 ′ + ⋯ + λ p
Principal_component_analysis
Tree-based ensemble machine learning methods
, Deng, X., and Huang, J. (2008) Feature weighting random forest for detection of hidden web search interfaces. Journal of Computational Linguistics
Random_forest
Open-source deep learning library
Deeplearning4j include network intrusion detection and cybersecurity, fraud detection for the financial sector, anomaly detection in industries such as manufacturing
Deeplearning4j
Similarity measure for number sequences
Regression Clustering Dimensionality reduction Density estimation Anomaly detection Data cleaning AutoML Association rules Semantic analysis Structured
Cosine_similarity
Numerical method that reduces the complexity of computationally intensive simulations
Regression Clustering Dimensionality reduction Density estimation Anomaly detection Data cleaning AutoML Association rules Semantic analysis Structured
Proper orthogonal decomposition
Proper_orthogonal_decomposition
Measurable property or characteristic
horizontal and vertical directions, number of internal holes, stroke detection and many others. In speech recognition, features for recognizing phonemes
Feature_(machine_learning)
Problem setup in machine learning
fields: image classification semantic segmentation image generation object detection natural language processing computational biology abstract reasoning Few-shot
Zero-shot_learning
Grouping a set of objects by similarity
imaging, and everyday tools like face detection and photo editing. Anomaly detection Anomalies and outliers are typically defined with respect to the clustering
Cluster_analysis
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