Relevant bibliographies by topics / Unsupervised anomaly detection

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Unsupervised anomaly detection'

Author: Grafiati
Published: 25 May 2024
Last updated: 31 July 2025

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Journal articles on the topic "Unsupervised anomaly detection"

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倪, 一鸣, and 松灿 陈. "Continual unsupervised anomaly detection." SCIENTIA SINICA Informationis 52, no. 1 (2022): 75. http://dx.doi.org/10.1360/ssi-2021-0192.

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Shi, Chengming, Bo Luo, Hongqi Li, Bin Li, Xinyong Mao, and Fangyu Peng. "Anomaly Detection via Unsupervised Learning for Tool Breakage Monitoring." International Journal of Machine Learning and Computing 6, no. 5 (2016): 256–59. http://dx.doi.org/10.18178/ijmlc.2016.6.5.607.

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Farzad, Amir, and T. Aaron Gulliver. "Unsupervised log message anomaly detection." ICT Express 6, no. 3 (2020): 229–37. http://dx.doi.org/10.1016/j.icte.2020.06.003.

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Almalawi, Abdulmohsen, Adil Fahad, Zahir Tari, et al. "Add-On Anomaly Threshold Technique for Improving Unsupervised Intrusion Detection on SCADA Data." Electronics 9, no. 6 (2020): 1017. http://dx.doi.org/10.3390/electronics9061017.

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Supervisory control and data acquisition (SCADA) systems monitor and supervise our daily infrastructure systems and industrial processes. Hence, the security of the information systems of critical infrastructures cannot be overstated. The effectiveness of unsupervised anomaly detection approaches is sensitive to parameter choices, especially when the boundaries between normal and abnormal behaviours are not clearly distinguishable. Therefore, the current approach in detecting anomaly for SCADA is based on the assumptions by which anomalies are defined; these assumptions are controlled by a par
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Goernitz, N., M. Kloft, K. Rieck, and U. Brefeld. "Toward Supervised Anomaly Detection." Journal of Artificial Intelligence Research 46 (February 20, 2013): 235–62. http://dx.doi.org/10.1613/jair.3623.

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Anomaly detection is being regarded as an unsupervised learning task as anomalies stem from adversarial or unlikely events with unknown distributions. However, the predictive performance of purely unsupervised anomaly detection often fails to match the required detection rates in many tasks and there exists a need for labeled data to guide the model generation. Our first contribution shows that classical semi-supervised approaches, originating from a supervised classifier, are inappropriate and hardly detect new and unknown anomalies. We argue that semi-supervised anomaly detection needs to gr
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Lok, Lai Kai, Vazeerudeen Abdul Hameed, and Muhammad Ehsan Rana. "Hybrid machine learning approach for anomaly detection." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 2 (2022): 1016–24. https://doi.org/10.11591/ijeecs.v27.i2.pp1016-1024.

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This research aims to improve anomaly detection performance by developing two variants of hybrid models combining supervised and unsupervised machine learning techniques. Supervised models cannot detect new or unseen types of anomaly. Hence in variant 1, a supervised model that detects normal samples is followed by an unsupervised learning model to screen anomaly. The unsupervised model is weak in differentiating between noise and fraud. Hence in variant 2, the hybrid model incorporates an unsupervised model that detects anomaly is followed by a supervised model to validate an anomaly. Three d
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Schmidl, Sebastian, Felix Naumann, and Thorsten Papenbrock. "AutoTSAD: Unsupervised Holistic Anomaly Detection for Time Series Data." Proceedings of the VLDB Endowment 17, no. 11 (2024): 2987–3002. http://dx.doi.org/10.14778/3681954.3681978.

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Detecting anomalous subsequences in time series data is one of the key tasks in time series analytics, having applications in environmental monitoring, preventive healthcare, predictive maintenance, and many further areas. Data scientists have developed various anomaly detection algorithms with individual strengths, such as the ability to detect repeating anomalies, anomalies in non-periodic time series, or anomalies with varying lengths. For a given dataset and task, the best algorithm with a suitable parameterization and, in some cases, sufficient training data, usually solves the anomaly de
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Tian, Yu, Haihua Liao, Jing Xu, Ya Wang, Shuai Yuan, and Naijin Liu. "Unsupervised Spectrum Anomaly Detection Method for Unauthorized Bands." Space: Science & Technology 2022 (February 21, 2022): 1–10. http://dx.doi.org/10.34133/2022/9865016.

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With the rapid development of wireless communication, spectrum plays increasingly important role in both military and civilian fields. Spectrum anomaly detection aims at detecting emerging anomaly signals and spectrum usage behavior in the environment, which is indispensable to secure safety and improve spectrum efficiency. However, spectrum anomaly detection faces many difficulties, especially for unauthorized frequency bands. In unauthorized bands, the composition of spectrum is complex and the anomaly usage patterns are unknown in prior. In this paper, a Variational Autoencoder- (VAE-) base
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Li, Xinliang, Jianmin Peng, Wenjing Li, Zhiping Song, and Xusheng Du. "Generative adversarial local density-based unsupervised anomaly detection." PLOS ONE 20, no. 1 (2025): e0315721. https://doi.org/10.1371/journal.pone.0315721.

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Anomaly detection is crucial in areas such as financial fraud identification, cybersecurity defense, and health monitoring, as it directly affects the accuracy and security of decision-making. Existing generative adversarial nets (GANs)-based anomaly detection methods overlook the importance of local density, limiting their effectiveness in detecting anomaly objects in complex data distributions. To address this challenge, we introduce a generative adversarial local density-based anomaly detection (GALD) method, which combines the data distribution modeling capabilities of GANs with local synt
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Lok, Lai Kai, Vazeerudeen Abdul Hameed, and Muhammad Ehsan Rana. "Hybrid machine learning approach for anomaly detection." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 2 (2022): 1016. http://dx.doi.org/10.11591/ijeecs.v27.i2.pp1016-1024.

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This research aims to <span lang="EN-US">improve anomaly detection performance by developing two variants of hybrid models combining supervised and unsupervised machine learning techniques. Supervised models cannot detect new or unseen types of anomaly. Hence in variant 1, a supervised model that detects normal samples is followed by an unsupervised learning model to screen anomaly. The unsupervised model is weak in differentiating between noise and fraud. Hence in variant 2, the hybrid model incorporates an unsupervised model that detects anomaly is followed by a supervised model to val
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Dissertations / Theses on the topic "Unsupervised anomaly detection"

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Mazel, Johan. "Unsupervised network anomaly detection." Thesis, Toulouse, INSA, 2011. http://www.theses.fr/2011ISAT0024/document.

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La détection d'anomalies est une tâche critique de l'administration des réseaux. L'apparition continue de nouvelles anomalies et la nature changeante du trafic réseau compliquent de fait la détection d'anomalies. Les méthodes existantes de détection d'anomalies s'appuient sur une connaissance préalable du trafic : soit via des signatures créées à partir d'anomalies connues, soit via un profil de normalité. Ces deux approches sont limitées : la première ne peut détecter les nouvelles anomalies et la seconde requiert une constante mise à jour de son profil de normalité. Ces deux aspects limitent
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Joshi, Vineet. "Unsupervised Anomaly Detection in Numerical Datasets." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1427799744.

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Di, Felice Marco. "Unsupervised anomaly detection in HPC systems." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019.

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Alla base di questo studio vi è l'analisi di tecniche non supervisionate applicate per il rilevamento di stati anomali in sistemi HPC, complessi calcolatori capaci di raggiungere prestazioni dell'ordine dei PetaFLOPS. Nel mondo HPC, per anomalia si intende un particolare stato che induce un cambiamento delle prestazioni rispetto al normale funzionamento del sistema. Le anomalie possono essere di natura diversa come il guasto che può riguardare un componente, una configurazione errata o un'applicazione che entra in uno stato inatteso provocando una prematura interruzione dei processi. I dataset
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Forstén, Andreas. "Unsupervised Anomaly Detection in Receipt Data." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-215161.

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With the progress of data handling methods and computing power comes the possibility of automating tasks that are not necessarily handled by humans. This study was done in cooperation with a company that digitalizes receipts for companies. We investigate the possibility of automating the task of finding anomalous receipt data, which could automate the work of receipt auditors. We study both anomalous user behaviour and individual receipts. The results indicate that automation is possible, which may reduce the necessity of human inspection of receipts.<br>Med de framsteg inom datahantering och
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Cheng, Leon. "Unsupervised topic discovery by anomaly detection." Thesis, Monterey, California: Naval Postgraduate School, 2013. http://hdl.handle.net/10945/37599.

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Approved for public release; distribution is unlimited<br>With the vast amount of information and public comment available online, it is of increasing interest to understand what is being said and what topics are trending online. Government agencies, for example, want to know what policies concern the public without having to look through thousands of comments manually. Topic detection provides automatic identification of topics in documents based on the information content and enhances many natural language processing tasks, including text summarization and information retrieval. Unsupervised
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Putina, Andrian. "Unsupervised anomaly detection : methods and applications." Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAT012.

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Une anomalie (également connue sous le nom de outlier) est une instance qui s'écarte de manière significative du reste des données et est définie par Hawkins comme "une observation, qui s'écarte tellement des autres observations qu'elle éveille les soupçons qu'il a été généré par un mécanisme différent". La détection d’anomalies (également connue sous le nom de détection de valeurs aberrantes ou de nouveauté) est donc le domaine de l’apprentissage automatique et de l’exploration de données dans le but d’identifier les instances dont les caractéristiques semblent être incohérentes avec le reste
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Audibert, Julien. "Unsupervised anomaly detection in time-series." Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS358.

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La détection d'anomalies dans les séries temporelles multivariées est un enjeu majeur dans de nombreux domaines. La complexité croissante des systèmes et l'explosion de la quantité de données ont rendu son automatisation indispensable. Cette thèse propose une méthode non supervisée de détection d'anomalies dans les séries temporelles multivariées appelée USAD. Cependant, les méthodes de réseaux de neurones profonds souffrent d'une limitation dans leur capacité à extraire des caractéristiques des données puisqu'elles ne s'appuient que sur des informations locales. Afin d'améliorer les performan
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Dani, Mohamed Cherif. "Unsupervised anomaly detection for aircraft health monitoring system." Thesis, Sorbonne Paris Cité, 2017. http://www.theses.fr/2017USPCB258.

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La limite des connaissances techniques ou fondamentale, est une réalité dont l’industrie fait face. Le besoin de mettre à jour cette connaissance acquise est essentiel pour une compétitivité économique, mais aussi pour une meilleure maniabilité des systèmes et machines. Aujourd’hui grâce à ces systèmes et machine, l’expansion de données en quantité, en fréquence de génération est un véritable phénomène. À présent par exemple, les avions Airbus génèrent des centaines de mégas de données par jour, et intègrent des centaines voire des milliers de capteurs dans les nouvelles générations d’avions.
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Dani, Mohamed Cherif. "Unsupervised anomaly detection for aircraft health monitoring system." Electronic Thesis or Diss., Sorbonne Paris Cité, 2017. http://www.theses.fr/2017USPCB258.

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La limite des connaissances techniques ou fondamentale, est une réalité dont l’industrie fait face. Le besoin de mettre à jour cette connaissance acquise est essentiel pour une compétitivité économique, mais aussi pour une meilleure maniabilité des systèmes et machines. Aujourd’hui grâce à ces systèmes et machine, l’expansion de données en quantité, en fréquence de génération est un véritable phénomène. À présent par exemple, les avions Airbus génèrent des centaines de mégas de données par jour, et intègrent des centaines voire des milliers de capteurs dans les nouvelles générations d’avions.
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Sarossy, George. "Anomaly detection in Network data with unsupervised learning methods." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-55096.

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Anomaly detection has become a crucial part of the protection of information and integrity. Due to the increase of cyber threats the demand for anomaly detection has grown for companies. Anomaly detection on time series data aims to detect unexpected behavior on the system. Anomalies often occur online, and companies need to be able to protect themselves from these intrusions. Multiple machine learning algorithms have been used and researched to solve the problem with anomaly detection and it is ongoing research to find the most optimal algorithms. Therefore, this study investigates algorithms
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Book chapters on the topic "Unsupervised anomaly detection"

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Deepak, P. "Anomaly Detection for Data with Spatial Attributes." In Unsupervised Learning Algorithms. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-24211-8_1.

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Angiulli, Fabrizio, Fabio Fassetti, Luca Ferragina, and Rosaria Spada. "Cooperative Deep Unsupervised Anomaly Detection." In Discovery Science. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-18840-4_23.

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Higuera, Juan Ramón Bermejo, Javier Bermejo Higuera, Juan Antonio Sicilia Montalvo, and Rubén González Crespo. "Unsupervised Approaches in Anomaly Detection." In Intelligent Systems Reference Library. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54038-7_3.

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Simarro Viana, Jaime, Ezequiel de la Rosa, Thijs Vande Vyvere, David Robben, Diana M. Sima, and CENTER-TBI Participants and Investigators. "Unsupervised 3D Brain Anomaly Detection." In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72084-1_13.

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Zhao, Zhiruo, Kishan G. Mehrotra, and Chilukuri K. Mohan. "Ensemble Algorithms for Unsupervised Anomaly Detection." In Current Approaches in Applied Artificial Intelligence. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19066-2_50.

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Zimmerer, David, Daniel Paech, Carsten Lüth, Jens Petersen, Gregor Köhler, and Klaus Maier-Hein. "Unsupervised Anomaly Detection in the Wild." In Informatik aktuell. Springer Fachmedien Wiesbaden, 2022. http://dx.doi.org/10.1007/978-3-658-36932-3_6.

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Graß, Alexander, Christian Beecks, and Jose Angel Carvajal Soto. "Unsupervised Anomaly Detection in Production Lines." In Machine Learning for Cyber Physical Systems. Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-662-58485-9_3.

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Haddad, Hatem, Feres Jerbi, and Issam Smaali. "Toward Unsupervised Energy Consumption Anomaly Detection." In IFIP Advances in Information and Communication Technology. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-63215-0_25.

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Eskin, Eleazar, Andrew Arnold, Michael Prerau, Leonid Portnoy, and Sal Stolfo. "A Geometric Framework for Unsupervised Anomaly Detection." In Advances in Information Security. Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0953-0_4.

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Syarif, Iwan, Adam Prugel-Bennett, and Gary Wills. "Unsupervised Clustering Approach for Network Anomaly Detection." In Networked Digital Technologies. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30507-8_13.

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Conference papers on the topic "Unsupervised anomaly detection"

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Li, Jiahao, Yiqiang Chen, Yunbing Xing, Yang Gu, and Xiangyuan Lan. "Contrast Memory for Unsupervised Anomaly Detection." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10888471.

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D’Amicantonio, Giacomo, Egor Bondarau, and Peter H. N. De With. "uTRAND: Unsupervised Anomaly Detection in Traffic Trajectories." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2024. http://dx.doi.org/10.1109/cvprw63382.2024.00759.

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Lagos, Juan, Haider Ali, Adnan Faroque, and Esa Rahtu. "Heterogeneous Datasets for Unsupervised Image Anomaly Detection." In 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2025. https://doi.org/10.1109/wacv61041.2025.00706.

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Wang, Zeyu, Tianxi Wu, Siyu Wu, Shuai Lu, and Hongwei Zhang. "Dual-Task-Based Unsupervised Fabric Anomaly Detection." In 2025 IEEE 14th Data Driven Control and Learning Systems (DDCLS). IEEE, 2025. https://doi.org/10.1109/ddcls66240.2025.11065691.

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Hossain, Mazharul, Aaron L. Robinson, Lan Wang, and Chrysanthe Preza. "Investigation of unsupervised and supervised hyperspectral anomaly detection." In Applications of Machine Learning 2024, edited by Barath Narayanan, Michael E. Zelinski, Tarek M. Taha, Abdul A. Awwal, and Khan M. Iftekharuddin. SPIE, 2024. http://dx.doi.org/10.1117/12.3029916.

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Li, Haoyang, Siwei Wang, Xinwang Liu, and Xinbiao Gan. "Unsupervised Graph Anomaly Detection on Directed Attribute Network." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10651283.

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Yu, Ting, Bo Ding, and Xu Wang. "Continual Adaptation for Unsupervised Time Series Anomaly Detection." In 2024 9th International Conference on Intelligent Computing and Signal Processing (ICSP). IEEE, 2024. http://dx.doi.org/10.1109/icsp62122.2024.10743267.

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Sadrolhefazi, Zeinab Sadat, Hadi Zare, Seyyed Amir Asghari, Parham Moradi, and Mahdi Jalili. "Unsupervised Graph-Based Anomaly Detection Using Variational Autoencoder." In 2024 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2024. https://doi.org/10.1109/icdmw65004.2024.00101.

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Neloy, Asif Ahmed, and Maxime Turgeon. "Disentangled Conditional Variational Autoencoder for Unsupervised Anomaly Detection." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825554.

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Bosnyaková, Bianka, František Babič, Tomáš Adam, and Anna Biceková. "Anomaly Detection in Blockchain Network Using Unsupervised Learning." In 2025 IEEE 23rd World Symposium on Applied Machine Intelligence and Informatics (SAMI). IEEE, 2025. https://doi.org/10.1109/sami63904.2025.10883055.

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