A review of novelty detection

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Anomaly detection using Autoencoders and Deep Convolution Generative Adversarial Networks

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Improved epileptic seizure detection combining dynamic feature normalization with EEG novelty detection

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Tamper-Proofing Video With Hierarchical Attention Autoencoder Hashing on Blockchain

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A linear time method for the detection of point and collective anomaliesPreprint

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Entropic One-Class Classifiers

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A Latent State-Based Multimodal Execution Monitor with Anomaly Detection and Classification for Robot Introspection

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Adaptive Online Learning With Regularized Kernel for One-Class Classification

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From one-class to two-class classification by incorporating expert knowledge: Novelty detection in human behaviour

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CNN-Based Volume Flow Rate Prediction of Oil–Gas–Water Three-Phase Intermittent Flow from Multiple Sensors

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Ontology-augmented Prognostics and Health Management for shopfloor-synchronised joint maintenance and production management decisions

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A linear time method for the detection of collective and point anomalies

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Detecting Cyberattacks on Electrical Storage Systems through Neural Network Based Anomaly Detection Algorithm

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Entropy-Based Economic Denial of Sustainability Detection

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Supervised Machine Learning Based Surface Inspection by Synthetizing Artificial Defects

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Novelty Detection with Autoencoders for System Health Monitoring in Industrial Environments

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On the Robustness of Open-World Test-Time Training: Self-Training with Dynamic Prototype Expansion

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Data-driven decision support system for the safety management of railway bridge networks

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Combining spectral and texture features in hyperspectral image analysis for plant monitoring

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Perfect Density Models Cannot Guarantee Anomaly Detection

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Outlier detection at the parcel-level in wheat and rapeseed crops using multispectral and SAR time series

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Learning Deep Classifiers Consistent with Fine-Grained Novelty Detection

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Gross outlier removal and fault data recovery for SHM data of dynamic responses by an annihilating filter‐based Hankel‐structured robust PCA method

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Unsupervised Out-of-Distribution Object Detection via PCA-Driven Dynamic Prototype Enhancement

Wu, A., Deng, C. D., & Liu, W. (2024) · IEEE Transactions on Image Processing · doi:10.1109/tip.2024.3378464 · Cited by 25

The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantization

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Concept-evolution detection in non-stationary data streams: a fuzzy clustering approach

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Model-Centered Assurance for Autonomous Systems

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Exploiting self-similarity for change detection

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The Power of GMMs: Unsupervised Dirt Spot Detection for Industrial Floor Cleaning Robots

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High-dimensional unsupervised classification via parsimonious contaminated mixtures

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Sample reduction using farthest boundary point estimation (FBPE) for support vector data description (SVDD)

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Universal Data Anomaly Detection via Inverse Generative Adversary Network

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

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One-Class Double Compression Detection of Advanced Videos Based on Simple Gaussian Distribution Model

Li, Q., Chen, S., Tan, S., Li, B., & Huang, J. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3069254 · Cited by 22

Word-level human interpretable scoring mechanism for novel text detection using Tsetlin Machines

Bhattarai, B., Granmo, O., & Jiao, L. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03281-1 · Cited by 23

Feature-Based Multi-Class Classification and Novelty Detection for Fault Diagnosis of Industrial Machinery

Calabrese, F., Regattieri, A., Bortolini, M., Galizia, F. G., & Visentini, L. (2021) · Applied Sciences · doi:10.3390/app11209580 · Cited by 22

A real-time adaptive network intrusion detection for streaming data: a hybrid approach

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Ball Screw Health Monitoring With Inertial Sensors

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A Probabilistic Transformation of Distance-Based Outliers

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On Applicability of IQR Method for Filtering of Experimental Data

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Scale-invariant anomaly detection with multiscale group-sparse models

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Reducing Anomaly Detection in Images to Detection in Noise

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Damage Detection in Beam Bridges Using Quasi-static Displacement Influence Lines

Zhang, S., & Liu, Y. (2019) · Applied Sciences · doi:10.3390/app9091805 · Cited by 22

Automated Change-Point Detection of EEG Signals Based on Structural Time-Series Analysis

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Anomaly Detection in EEG Signals: A Case Study on Similarity Measure

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A flexible alarm prediction system for smart manufacturing scenarios following a forecaster–analyzer approach

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A Transfer Learning Framework for Anomaly Detection Using Model of Normality

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Online learning on tiny micro-controllers for anomaly detection in water distribution systems

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Generative adversarial networks-enabled anomaly detection systems: A survey

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Conditional classifiers and boosted conditional Gaussian mixture model for novelty detection

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Center-Aware Adversarial Autoencoder for Anomaly Detection

Li, D., Tao, Q., Liu, J., & Wang, H. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3122179 · Cited by 22

Current Only-Based Fault Diagnosis Method for Industrial Robot Control Cables

Kim, H., Lee, H., & Kim, S. W. (2022) · Sensors · doi:10.3390/s22051917 · Cited by 21

Normality-calibrated autoencoder for unsupervised anomaly detection on data contamination

Yu, J., Kim, M., Kim, J., & Oh, H. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.132249 · Cited by 26

Use of a roving computer vision system to compare anomaly detection techniques for health monitoring of bridges

Lydon, D., Kromanis, R., Lydon, M., Early, J. M., & Taylor, S. E. (2022) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-022-00617-w · Cited by 21

A Survey of Botnet-Based DDoS Flooding Attacks of Application Layer

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Detection of anomalous behavior in a robot system based on deep learning elements

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A Smartphone-Based System for Detecting Falls Using Anomaly Detection

Carletti, V., Greco, A., Saggese, A., & Vento, M. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-68548-9_45 · Cited by 21

Random Finite Set-Based Anomaly Detection for Safety Monitoring in Construction Sites

Kamoona, A. M., Gostar, A. K., Tennakoon, R., Bab‐Hadiashar, A., Accadia, D., Thorpe, J., & Hoseinnezhad, R. (2019) · IEEE Access · doi:10.1109/access.2019.2932137 · Cited by 21

A Framework for Building Uncertainty Wrappers for AI/ML-Based Data-Driven Components

Kläs, M., & Jöckel, L. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-55583-2_23 · Cited by 21

Road User Abnormal Trajectory Detection using a Deep AutoencoderPreprint

Roy, P. R., & Bilodeau, G.-A. (2018) · EasyChair preprint · doi:10.29007/xwfw · Cited by 21

The Clever Hans Effect in Anomaly DetectionPreprint

Kauffmann, J., Ruff, L., Montavon, G., & Müller, K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.10609 · Cited by 39

Digital Twins and Civil Engineering Phases: Reorienting Adoption Strategies

Adebiyi, T. A., Ajenifuja, N. A., & Zhang, R. (2024) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.4066181 · Cited by 26

Self-Improving Generative Artificial Neural Network for Pseudorehearsal Incremental Class Learning

Mellado, D., Saavedra, C., Chabert, S., Torres, R., & Rodrigo Salas, F. (2019) · Algorithms · doi:10.3390/a12100206 · Cited by 20

Online sequential attack detection for ADS-B data based on hierarchical temporal memory

Li, T., Wang, B., Shang, F., Tian, J., & Cao, K. (2019) · Computers & Security · doi:10.1016/j.cose.2019.101599 · Cited by 20

Open-World Class Discovery with Kernel Networks

Wang, Z., Salehi, B., Gritsenko, A., Chowdhury, K. R., Ioannidis, S., & Dy, J. (2020) · Proceedings · doi:10.1109/icdm50108.2020.00072 · Cited by 22

Conformal Kernel Expected Similarity for Anomaly Detection in Time-Series data

Safin, A., & Burnaev, E. (2017) · Russian Agency for Digital Standardization · doi:10.25728/assa.2017.17.3.497 · Cited by 20

Multikernel correntropy based robust least squares one-class support vector machine

Zheng, Y., Wang, S., & Chen, B. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126324 · Cited by 20

Personalized alerts for patients with COPD using pulse oximetry and symptom scores

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One-class classification of point patterns of extremes

Luca, S., Clifton, D. A., & Vanrumste, B. (2016) · Ghent University Academic Bibliography (Ghent University) · Cited by 20

Finding the Unknown: Novelty Detection with Extreme Value Signatures of Deep Neural Activations

Schultheiß, A., Käding, C., Freytag, A., & Denzler, J. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66709-6_19 · Cited by 20

Anomaly Detection in Activities of Daily Living Using One-Class Support Vector Machine

Yahaya, S. W., Langensiepen, C. S., & Lotfi, A. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-97982-3_30 · Cited by 20

Structural health monitoring using high-dimensional features from time series modeling by innovative hybrid distance-based methods

Daneshvar, M. H., Gharighoran, A., Zareei, S. A., & Karamodin, A. (2021) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-020-00466-5 · Cited by 20

Deep embeddings for novelty detection in myopathy

Burlina, P., Joshi, N., Billings, S., Wang, I.-J., & Albayda, J. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.12.006 · Cited by 20

ODE - Augmented Training Improves Anomaly Detection in Sensor Data from MachinesPreprint

Yadav, M., Malhotra, P., Vig, L., Sriram, K., & Shroff, G. M. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.01534 · Cited by 20

Selective Classification Can Magnify Disparities Across GroupsPreprint

Jones, E., Sagawa, S., Koh, P. W., Kumar, A., & Liang, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.14134 · Cited by 34

Feature selection for unsupervised defect detection of a wind turbine blade considering operational and environmental conditions

Ashkarkalaei, M., Ghiasi, R., Pakrashi, V., & Malekjafarian, A. (2025) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2025.112568 · Cited by 19

Deep anomaly detection for time series: A survey

Jia, X., Peng, X., Peng, W., Zhao, B., Li, H., & Shen, C. (2025) · Computer Science Review · doi:10.1016/j.cosrev.2025.100787 · Cited by 27

A novelty detection scheme for rolling bearing based on multiscale fuzzy distribution entropy and hybrid kernel convex hull approximation

Zhao, X., Cheng, J., Wang, P., He, Z., Shao, H., & Yang, Y. (2020) · Measurement · doi:10.1016/j.measurement.2020.107589 · Cited by 19

Anomaly detection using state‐space models and reinforcement learning

Khazaeli, S., Nguyen, L. H., & Goulet, J. A. (2021) · Structural Control and Health Monitoring · doi:10.1002/stc.2720 · Cited by 19

Machine Learning Approaches to Identifying Changes in Eruptive State Using Multi‐Parameter Datasets From the 2006 Eruption of Augustine Volcano, Alaska

Manley, G. F., Mather, T. A., Pyle, D. M., Clifton, D. A., Rodgers, M., Thompson, G., & Roman, D. C. (2021) · Journal of Geophysical Research Solid Earth · doi:10.1029/2021jb022323 · Cited by 19

A Smart Capacitive Sensor Skin with Embedded Data Quality Indication for Enhanced Safety in Human–Robot Interaction

Scholl, C., Tobola, A., Ludwig, K., Zanca, D., & Eskofier, B. M. (2021) · Sensors · doi:10.3390/s21217210 · Cited by 19

Lightweight Voice Spoofing Detection Using Improved One-Class Learning and Knowledge Distillation

Ren, Y., Peng, H., Li, L., & Yang, Y. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3321505 · Cited by 20

Identifying the validity domain of machine learning models in building energy systems

Rätz, M., Henkel, P., Stoffel, P., Streblow, R., & Müller, D. (2023) · Energy and AI · doi:10.1016/j.egyai.2023.100324 · Cited by 23

An Evaluation of Supervised, Novelty-Based and Hybrid Approaches to Fall Detection Using Silmee Accelerometer Data

Lisowska, A., Wheeler, G., Inza, V. C., & Poole, I. (2015) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2015.60 · Cited by 19

A Statistical Framework for Automatic Leakage Detection in Smart Water and Gas Grids

Fagiani, M., Squartini, S., Gabrielli, L., Severini, M., & Piazza, F. (2016) · Energies · doi:10.3390/en9090665 · Cited by 19

Big data analytics for proactive industrial decision support

Atzmueller, M., Klöpper, B., Mawla, H. A., Jäschke, B., Hollender, M., Graube, M., Arnu, D., Schmidt, A. U., Heinze, S., Schorer, L., et al. (2016) · atp magazin · doi:10.17560/atp.v58i09.2315 · Cited by 19

An integrated framework for statistical change detection in running status of industrial machinery under transient conditions

Chen, G., Lu, G., Liu, J., & Yan, P. (2019) · ISA Transactions · doi:10.1016/j.isatra.2019.03.026 · Cited by 19

Data-driven CBM tool for risk-informed decision-making in an electric arc furnace

Fumagalli, L., Cattaneo, L., Roda, I., Macchi, M., & Rondi, M. (2019) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-019-04189-w · Cited by 19

Generative Adversarial Network Based Anomaly Detection on the Benchmark Tennessee Eastman Process

Yang, X. P. O., & Feng, D. (2019) · International Conference on Control, Automation and Robotics (ICCAR) · doi:10.1109/iccar.2019.8813415 · Cited by 19

Modular ensembles for one-class classification based on density analysis

Liu, J., Miao, Q., Sun, Y., Song, J., & Quan, Y. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.06.037 · Cited by 19

Informed Democracy: Voting-based Novelty Detection for Action RecognitionPreprint

Roitberg, A., Al-Halah, Z., & Stiefelhagen, R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.12819 · Cited by 27

Deep Gaussian Process autoencoders for novelty detection

Domingues, R., Pietro Michiardi, A., Zouaoui, J., & Filippone, M. (2018) · Machine Learning · doi:10.1007/s10994-018-5723-3 · Cited by 16

Approximate training of one-class support vector machines using expected margin

Kang, S., Kim, D., & Cho, S. (2019) · Computers & Industrial Engineering · doi:10.1016/j.cie.2019.03.029 · Cited by 18

Palisade: A framework for anomaly detection in embedded systems

Kauffman, S., Dunne, M., Gracioli, G., Khan, W., Benann, N., & Fischmeister, S. (2020) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2020.101876 · Cited by 18

Anomaly Detection Methods in Autonomous Robotic Missions

Nandakumar, S. C., Mitchell, D., Erden, M. S., Flynn, D., & Lim, T. (2024) · Sensors · doi:10.3390/s24041330 · Cited by 23

Converting SVDD scores into probability estimates: Application to outlier detection

Azami, M. E., Lartizien, C., & Canu, S. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.01.103 · Cited by 18

www.elsevier.com/locate/sigpro Noveltydetection: a review—part 1: statistical approaches

Markou, M., & Singh, S. (2003)

Novelty detection: a review—part 1: statistical approaches

Markou, M., & Singh, S. (2003) · Signal Processing · doi:10.1016/j.sigpro.2003.07.018 · Cited by 1,472

Novelty detection: a review—part 2:

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