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Dissertations / Theses on the topic 'Unsupervised anomaly detection'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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1

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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5

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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10

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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Lindgren, Erik, and Niklas Allard. "Exploring unsupervised anomaly detection in Bill of Materials structures." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160262.

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Siemens produce a variety of different products that provide innovative solutions within different areas such as electrification, automation and digitalization, some of which are turbine machines. During the process of creating or modifying a machine, it is vital that the documentation used as reference is trustworthy and complete. If the documentation is incomplete during the process, the risk of delivering faulty machines to customers drastically increases, causing potential harm to Siemens. This thesis aims to explore the possibility of finding anomalies in Bill of Material structures, in o

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Vendramin, Nicoló. "Unsupervised Anomaly Detection on Multi-Process Event Time Series." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254885.

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Establishing whether the observed data are anomalous or not is an important task that has been widely investigated in literature, and it becomes an even more complex problem if combined with high dimensional representations and multiple sources independently generating the patterns to be analyzed. The work presented in this master thesis employs a data-driven pipeline for the definition of a recurrent auto-encoder architecture to analyze, in an unsupervised fashion, high-dimensional event time-series generated by multiple and variable processes interacting with a system. Facing the above menti

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Granlund, Oskar. "Unsupervised anomaly detection on log-based time series data." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-265534.

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Due to a constant increase in the number of connected devices and there is an increased demand for confidentiality, availability, and integrity on applications. This thesis was focused on unsupervised anomaly detection in data centers. It evaluates how suitable open source state-of-the-art solutions are at finding abnormal trends and patterns in log-based data streams. The methods used in this work are Principal component analysis (PCA), LogCluster, and Hierarchical temporal memory (HTM). They were evaluated using F-score on a real data set from an Apache access log. The data set was carefully

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Leto, Kevin. "Anomaly detection in HPC systems." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019.

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Nell’ambito dei supercomputer, l’attività di anomaly detection rappresenta un’ottima strategia per mantenere alte le performance del sistema (disponibilità ed affidabilità), consentendo di prevenire i guasti e di adattare l’attività di manutenzione alla salute del sistema stesso. Il supercomputer esaminato da questa ricerca è chiamato MARCONI ed appartiene al CINECA, consorzio interuniversitario italiano con sede a Bologna. I dati estratti per l’analisi si riferiscono in particolar modo al nodo “r183c12s04”, ma per provare la generalità dell’approccio sono stati eseguiti ulteriori test anche

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Beach, David J. "Anomaly Detection with Advanced Nonlinear Dimensionality Reduction." Digital WPI, 2020. https://digitalcommons.wpi.edu/etd-theses/1378.

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Dimensionality reduction techniques such as t-SNE and UMAP are useful both for overview of high-dimensional datasets and as part of a machine learning pipeline. These techniques create a non-parametric model of the manifold by fitting a density kernel about each data point using the distances to its k-nearest neighbors. In dense regions, this approach works well, but in sparse regions, it tends to draw unrelated points into the nearest cluster. Our work focuses on a homotopy method which imposes graph-based regularization over the manifold parameters to update the embedding. As the homotopy pa

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Fröjdholm, Hampus. "Learning from 3D generated synthetic data for unsupervised anomaly detection." Thesis, Uppsala universitet, Avdelningen för visuell information och interaktion, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-443243.

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Modern machine learning methods, utilising neural networks, require a lot of training data. Data gathering and preparation has thus become a major bottleneck in the machine learning pipeline and researchers often use large public datasets to conduct their research (such as the ImageNet [1] or MNIST [2] datasets). As these methods begin being used in industry, these challenges become apparent. In factories objects being produced are often unique and may even involve trade secrets and patents that need to be protected. Additionally, manufacturing may not have started yet, making real data collec

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Azmoudeh, Fard Simon. "Anomaly Detection in Networks using Autoencoder and Unsupervised Learning Methods." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-55097.

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The increasing popularity of networking devices at workplaces leads to an exponential increase in the frequency of network attacks. This leads to having protected networks being more and more important. Because of the increase in network activity workplaces have started to leave anomaly detection in the hands of artificial intelligence. However, the current methods of detecting anomalies can not accurately detect all of them. In this thesis, I propose a training method for autoencoders that shows how k-Means Clustering can be combined with an autoencoder for feature extraction with the use of

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Wu, Xinheng. "A Deep Unsupervised Anomaly Detection Model for Automated Tumor Segmentation." Thesis, The University of Sydney, 2020. https://hdl.handle.net/2123/22502.

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Many researches have been investigated to provide the computer aided diagnosis (CAD) automated tumor segmentation in various medical images, e.g., magnetic resonance (MR), computed tomography (CT) and positron-emission tomography (PET). The recent advances in automated tumor segmentation have been achieved by supervised deep learning (DL) methods trained on large labelled data to cover tumor variations. However, there is a scarcity in such training data due to the cost of labeling process. Thus, with insufficient training data, supervised DL methods have difficulty in generating effective feat

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Larsson, Frans. "Algorithmic trading surveillance : Identifying deviating behavior with unsupervised anomaly detection." Thesis, Uppsala universitet, Matematiska institutionen, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-389941.

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The financial markets are no longer what they used to be and one reason for this is the breakthrough of algorithmic trading. Although this has had several positive effects, there have been recorded incidents where algorithms have been involved. It is therefore of interest to find effective methods to monitor algorithmic trading. The purpose of this thesis was therefore to contribute to this research area by investigating if machine learning can be used for detecting deviating behavior. Since the real world data set used in this study lacked labels, an unsupervised anomaly detection approach wa

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Haddad, Josef, and Carl Piehl. "Unsupervised anomaly detection in time series with recurrent neural networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-259655.

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Artificial neural networks (ANN) have been successfully applied to a wide range of problems. However, most of the ANN-based models do not attempt to model the brain in detail, but there are still some models that do. An example of a biologically constrained ANN is Hierarchical Temporal Memory (HTM). This study applies HTM and Long Short-Term Memory (LSTM) to anomaly detection problems in time series in order to compare their performance for this task. The shape of the anomalies are restricted to point anomalies and the time series are univariate. Pre-existing implementations that utilise these

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Sreenivasulu, Ajay. "Evaluation of cluster based Anomaly detection." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-18053.

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Anomaly detection has been widely researched and used in various application domains such as network intrusion, military, and finance, etc. Anomalies can be defined as an unusual behavior that differs from the expected normal behavior. This thesis focuses on evaluating the performance of different clustering algorithms namely k-Means, DBSCAN, and OPTICS as an anomaly detector. The data is generated using the MixSim package available in R. The algorithms were tested on different cluster overlap and dimensions. Evaluation metrics such as Recall, precision, and F1 Score were used to analyze the p

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Fockstedt, Jonas, and Ema Krcic. "Unsupervised anomaly detection for structured data - Finding similarities between retail products." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-44756.

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Data is one of the most contributing factors for modern business operations. Having bad data could therefore lead to tremendous losses, both financially and for customer experience. This thesis seeks to find anomalies in real-world, complex, structured data, causing an international enterprise to miss out on income and the potential loss of customers. By using graph theory and similarity analysis, the findings suggest that certain countries contribute to the discrepancies more than other countries. This is believed to be an effect of countries customizing their products to match the market’s n

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Renström, Martin, and Timothy Holmsten. "Fraud Detection on Unlabeled Data with Unsupervised Machine Learning." Thesis, KTH, Hälsoinformatik och logistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230592.

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A common problem in systems handling user interaction was the risk for fraudulent behaviour. As an example, in a system with credit card transactions it could have been a person using a another user's account for purchases, or in a system with advertisment it could be bots clicking on ads. These malicious attacks were often disguised as normal interactions and could be difficult to detect. It was especially challenging when working with datasets that did not contain so called labels, which showed if the data point was fraudulent or not. This meant that there were no data that had previously be

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Merrill, Nicholas Swede. "Modified Kernel Principal Component Analysis and Autoencoder Approaches to Unsupervised Anomaly Detection." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/98659.

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Unsupervised anomaly detection is the task of identifying examples that differ from the normal or expected pattern without the use of labeled training data. Our research addresses shortcomings in two existing anomaly detection algorithms, Kernel Principal Component Analysis (KPCA) and Autoencoders (AE), and proposes novel solutions to improve both of their performances in the unsupervised settings. Anomaly detection has several useful applications, such as intrusion detection, fault monitoring, and vision processing. More specifically, anomaly detection can be used in autonomous driving to ide

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Tjhai, Gina C. "Anomaly-based correlation of IDS alarms." Thesis, University of Plymouth, 2011. http://hdl.handle.net/10026.1/308.

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An Intrusion Detection System (IDS) is one of the major techniques for securing information systems and keeping pace with current and potential threats and vulnerabilities in computing systems. It is an indisputable fact that the art of detecting intrusions is still far from perfect, and IDSs tend to generate a large number of false IDS alarms. Hence human has to inevitably validate those alarms before any action can be taken. As IT infrastructure become larger and more complicated, the number of alarms that need to be reviewed can escalate rapidly, making this task very difficult to manage. T

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Vidmark, Anton. "CONSTRUCTING AND VARYING DATA MODELS FOR UNSUPERVISED ANOMALY DETECTION ON LOG DATAData modelling and domain knowledge’s impact on anomaly detection and explainability." Thesis, Umeå universitet, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-163544.

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As the complexity of today’s systems increases, manual system monitoring and log file analysis are no longer applicable, giving an increasing need for automated anomaly detection systems. However, most current research in the domain, tend to focus only on the technical details of the frameworks and the evaluations of the algorithms, and how this impacts anomaly detection results. In contrast, this study emphasizes the details of how one can approach to understand and model the data, and how this impact anomaly detection performance.Given log data from an education platform application, data i

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Alaverdyan, Zaruhi. "Unsupervised representation learning for anomaly detection on neuroimaging. Application to epilepsy lesion detection on brain MRI." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEI005/document.

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Cette étude vise à développer un système d’aide au diagnostic (CAD) pour la détection de lésions épileptogènes, reposant sur l’analyse de données de neuroimagerie, notamment, l’IRM T1 et FLAIR. L’approche adoptée, introduite précédemment par Azami et al., 2016, consiste à placer la tâche de détection dans le cadre de la détection de changement à l'échelle du voxel, basée sur l’apprentissage d’un modèle one-class SVM pour chaque voxel dans le cerveau. L'objectif principal de ce travail est de développer des mécanismes d’apprentissage de représentations, qui capturent les informations les plus d

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Lindroth, Henriksson Amelia. "Unsupervised Anomaly Detection on Time Series Data: An Implementation on Electricity Consumption Series." Thesis, KTH, Matematisk statistik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301731.

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Digitization of the energy industry, introduction of smart grids and increasing regulation of electricity consumption metering have resulted in vast amounts of electricity data. This data presents a unique opportunity to understand the electricity usage and to make it more efficient, reducing electricity consumption and carbon emissions. An important initial step in analyzing the data is to identify anomalies. In this thesis the problem of anomaly detection in electricity consumption series is addressed using four machine learning methods: density based spatial clustering for applications with

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Jernbäcker, Carl. "Unsupervised real-time anomaly detection on streaming data for large-scale application deployments." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-262681.

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Anomaly detection is the classification of data points that do not adhere to the familiar pattern; in previous studies there existed a need for extensive human interactions with either labelling or sorting normal and abnormal data from one another. In this thesis, we want to go one step further and apply machine learning techniques on time-series data in order to have a deeper understanding of the properties of a given data point without any sorting and labelling. In this thesis, a method is presented that can successfully find anomalies in both real and synthetic datasets. The method uses a c

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Bracci, Lorenzo, and Amirhossein Namazi. "EVALUATION OF UNSUPERVISED MACHINE LEARNING MODELS FOR ANOMALY DETECTION IN TIME SERIES SENSOR DATA." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-299734.

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With the advancement of the internet of things and the digitization of societies sensor recording time series data can be found in an always increasing number of places including among other proximity sensors on cars, temperature sensors in manufacturing plants and motion sensors inside smart homes. This always increasing reliability of society on these devices lead to a need for detecting unusual behaviour which could be caused by malfunctioning of the sensor or by the detection of an uncommon event. The unusual behaviour mentioned is often referred to as an anomaly. In order to detect anomal

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Mathur, Nitin O. "Application of Autoencoder Ensembles in Anomaly and Intrusion Detection using Time-Based Analysis." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin161374876195402.

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Hanna, Peter, and Erik Swartling. "Anomaly Detection in Time Series Data using Unsupervised Machine Learning Methods: A Clustering-Based Approach." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273630.

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For many companies in the manufacturing industry, attempts to find damages in their products is a vital process, especially during the production phase. Since applying different machine learning techniques can further aid the process of damage identification, it becomes a popular choice among companies to make use of these methods to enhance the production process even further. For some industries, damage identification can be heavily linked with anomaly detection of different measurements. In this thesis, the aim is to construct unsupervised machine learning models to identify anomalies on un

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Sivaramakrishnan, Jayaram. "Unsupervised probabilistic and kernel regression methods for anomaly detection and parameter margin prediction of industrial design." Thesis, Sivaramakrishnan, Jayaram (2021) Unsupervised probabilistic and kernel regression methods for anomaly detection and parameter margin prediction of industrial design. PhD thesis, Murdoch University, 2021. https://researchrepository.murdoch.edu.au/id/eprint/62536/.

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One of the significant challenges facing industrial plant design is ensuring the integrity of massive design datasets generated during the project execution. This work is motivated by personal experience of data integrity issues during projects caused by insufficient automation affecting the quality of deliverables. Therefore, this project sought automated solutions for detecting anomalies in industrial design data in the form of outliers. Several novel methods are proposed, based on the Hidden Markov Model (HMM) and a modified General Regression Neural Network called the Margin-Based GRNN (MB

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Minarini, Francesco. "Anomaly detection prototype for log-based predictive maintenance at INFN-CNAF tier-1." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19304/.

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Splitting the evolution of HEP from the one of computational resources needed to perform analyses is, nowadays, not possible. Each year, in fact, LHC produces dozens of PetaBytes of data (e.g. collision data, particle simulation, metadata etc.) that need orchestrated computing resources for storage, computational power and high throughput networks to connect centers. As a consequence of the LHC upgrade, the Luminosity of the experiment will increase by a factor of 10 over its originally designed value, entailing a non negligible technical challenge at computing centers: it is expected, in fact

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Labonne, Maxime. "Anomaly-based network intrusion detection using machine learning." Electronic Thesis or Diss., Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAS011.

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Ces dernières années, le piratage est devenu une industrie à part entière, augmentant le nombre et la diversité des cyberattaques. Les menaces qui pèsent sur les réseaux informatiques vont des logiciels malveillants aux attaques par déni de service, en passant par le phishing et l'ingénierie sociale. Un plan de cybersécurité efficace ne peut plus reposer uniquement sur des antivirus et des pare-feux pour contrer ces menaces : il doit inclure plusieurs niveaux de défense. Les systèmes de détection d'intrusion (IDS) réseaux sont un moyen complémentaire de renforcer la sécurité, avec la possibili

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Pierrau, Magnus. "Evaluating Unsupervised Methods for Out-of-Distribution Detection on Semantically Similar Image Data." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302583.

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Out-of-distribution detection considers methods used to detect data that deviates from the underlying data distribution used to train some machine learning model. This is an important topic, as artificial neural networks have previously been shown to be capable of producing arbitrarily confident predictions, even for anomalous samples that deviate from the training distribution. Previous work has developed many reportedly effective methods for out-of-distribution detection, but these are often evaluated on data that is semantically different from the training data, and therefore does not neces

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Kommineni, Sri Sai Manoj, and Akhila Dindi. "Automating Log Analysis." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-21175.

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Background: With the advent of the information age, there are many large numbers of services rising which run on several clusters of computers. Maintaining such large complex systems is a very difficult task. Developers use one tool which is common for almost all software systems, they are the console logs. To troubleshoot problems, developers refer to these logs to solve the issue. Identifying anomalies in the logs would lead us to the cause of the problem, thereby automating the analysis of logs. This study focuses on anomaly detection in logs. Objectives: The main goal of the thesis is to

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ABUKMEIL, MOHANAD. "UNSUPERVISED GENERATIVE MODELS FOR DATA ANALYSIS AND EXPLAINABLE ARTIFICIAL INTELLIGENCE." Doctoral thesis, Università degli Studi di Milano, 2022. http://hdl.handle.net/2434/889159.

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For more than a century, the methods of learning representation and the exploration of the intrinsic structures of data have developed remarkably and currently include supervised, semi-supervised, and unsupervised methods. However, recent years have witnessed the flourishing of big data, where typical dataset dimensions are high, and the data can come in messy, missing, incomplete, unlabeled, or corrupted forms. Consequently, discovering and learning the hidden structure buried inside such data becomes highly challenging. From this perspective, latent data analysis and dimensionality reductio

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Avdic, Adnan, and Albin Ekholm. "Anomaly Detection in an e-Transaction System using Data Driven Machine Learning Models : An unsupervised learning approach in time-series data." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18421.

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Background: Detecting anomalies in time-series data is a task that can be done with the help of data driven machine learning models. This thesis will investigate if, and how well, different machine learning models, with an unsupervised approach,can detect anomalies in the e-Transaction system Ericsson Wallet Platform. The anomalies in our domain context is delays on the system. Objectives: The objectives of this thesis work is to compare four different machine learning models ,in order to find the most relevant model. The best performing models are decided by the evaluation metric F1-score. An

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Baur, Christoph [Verfasser], Nassir [Akademischer Betreuer] Navab, Nassir [Gutachter] Navab, and Ben [Gutachter] Glocker. "Anomaly Detection in Brain MRI: From Supervised to Unsupervised Deep Learning / Christoph Baur ; Gutachter: Nassir Navab, Ben Glocker ; Betreuer: Nassir Navab." München : Universitätsbibliothek der TU München, 2021. http://d-nb.info/1236343115/34.

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Manovi, Livia. "Machine Learning Unsupervised Methods in the Design of an On-board Health Monitoring System for Satellite Applications." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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The dissertation starts by providing a description of the phenomena related to the increasing importance recently acquired by satellite applications. The spread of such technology comes with implications, such as an increase in maintenance cost, from which derives the interest in developing advanced techniques that favor an augmented autonomy of spacecrafts in health monitoring. Machine learning techniques are widely employed to lay a foundation for effective systems specialized in fault detection by examining telemetry data. Telemetry consists of a considerable amount of information; theref

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Dalvi, Aditi. "Performance of One-class Support Vector Machine (SVM) in Detection of Anomalies in the Bridge Data." University of Cincinnati / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin150478019017791.

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Ait, Saada Mira. "Unsupervised learning from textual data with neural text representations." Electronic Thesis or Diss., Université Paris Cité, 2023. http://www.theses.fr/2023UNIP7122.

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L'ère du numérique génère des quantités énormes de données non structurées telles que des images et des documents, nécessitant des méthodes de traitement spécifiques pour en tirer de la valeur. Les données textuelles présentent une difficulté supplémentaire car elles ne contiennent pas de valeurs numériques. Les plongements de mots sont des techniques permettant de transformer automatiquement du texte en données numériques, qui permettent aux algorithmes d'apprentissage automatique de les traiter. Les tâches non-supervisées sont un enjeu majeur dans l'industrie car elles permettent de créer de

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Formato, Lorenzo. "IDENTIFICAZIONE DI GUASTI TRAMITE ALGORITMI DI CLASSIFICAZIONE & CLUSTERING per applicazioni di Manutenzione Predittiva in Scenari di Industria 4.0." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23028/.

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L'elaborato della presente tesi tratta l'applicazione dei modelli di Machine Learning all'interno di un banco di test per assali elettrici; una soluzione dedicata al mondo dell'Industria 4.0. Il progetto di tesi prevede l'utilizzo di modelli di Classificazione (Logistic Regression, SVM: Support Vector Machine, Naive Bayes, Decision Tree e Random Forest) e di Clustering (K-Means e Agglomerative) per l'identificazione dei comportamenti normali e attesi durante la fase di test. L'obiettivo finale della trattazione è dunque quello di riuscire ad ottenere un modello capace di identificare sit

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Hamid, Muhammad Raffay. "A computational framework for unsupervised analysis of everyday human activities." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/24765.

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Thesis (Ph.D.)--Computing, Georgia Institute of Technology, 2009.<br>Committee Chair: Aaron Bobick; Committee Member: Charles Isbell; Committee Member: David Hogg; Committee Member: Irfan Essa; Committee Member: James Rehg

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Boniol, Paul. "Detection of anomalies and identification of their precursors in large data series collections." Electronic Thesis or Diss., Université Paris Cité, 2021. http://www.theses.fr/2021UNIP5206.

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Les larges collections de séries temporelles deviennent une réalité dans un grand nombre de domaines scientifiques et sociaux, comme la finance, les sciences de l’environnement, l’astrophysique, les neurosciences, l’ingénierie ou les métiers du numérique. Il y a donc un intérêt et un besoin de plus en plus importants de développer des techniques efficaces pour analyser et traiter ce type de données. De manière informelle, une série temporelle est une séquence ordonnée de points ou de valeurs. Une fois les séries collectées et disponibles, les utilisateurs ont souvent besoin de les étudier pour

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OLIVEIRA, Paulo César de. "Abordagem semi-supervisionada para detecção de módulos de software defeituosos." Universidade Federal de Pernambuco, 2015. https://repositorio.ufpe.br/handle/123456789/19990.

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Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2017-07-24T12:11:04Z No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) Dissertação Mestrado Paulo César de Oliveira.pdf: 2358509 bytes, checksum: 36436ca63e0a8098c05718bbee92d36e (MD5)<br>Made available in DSpace on 2017-07-24T12:11:04Z (GMT). No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) Dissertação Mestrado Paulo César de Oliveira.pdf: 2358509 bytes, checksum: 36436ca63e0a8098c05718bbee92d36e (MD5) Previous issue date: 2015-08-3

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Boussik, Amine. "Apprentissage profond non-supervisé : Application à la détection de situations anormales dans l’environnement du train autonome." Electronic Thesis or Diss., Valenciennes, Université Polytechnique Hauts-de-France, 2023. http://www.theses.fr/2023UPHF0040.

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La thèse aborde les défis du monitoring de l’environnement et de détection des anomalies, notamment des obstacles, pour un train de fret autonome. Bien que traditionnellement, les transports ferroviaires étaient sous la supervision humaine, les trains autonomes offrent des perspectives d’avantages en termes de coûts, de temps et de sécurité. Néanmoins, leur exploitation dans des environnements complexes pose d’importants enjeux de sûreté. Au lieu d’une approche supervisée nécessitant des données annotées onéreuses et limitées, cette recherche adopte une technique non supervisé

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Boutahala, Ramzi. "Mécanismes de sécurisation des communications véhiculaires." Electronic Thesis or Diss., Reims, 2023. http://www.theses.fr/2023REIMS047.

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Dans cette thèse, nous examinons le problème de la surcharge des canaux de communication dans le contexte des systèmes de transport intelligents coopératifs (C-ITS). Nous visons à améliorer le mécanisme de communication entre les véhicules et nous nous concentrons sur la partie sécurité de la communication, qui est la plus coûteuse en termes de ressources. En Europe et aux États-Unis, des protocoles de communication adaptés ont été proposés pour assurer la communication et la coopération entre tous les acteurs concernés (véhicules, infrastructure routière, piétons, etc.). Ces protocoles permet

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Cherdo, Yann. "Détection d'anomalie non supervisée sur les séries temporelle à faible coût énergétique utilisant les SNNs." Electronic Thesis or Diss., Université Côte d'Azur, 2024. http://www.theses.fr/2024COAZ4018.

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Dans le cadre de la maintenance prédictive du constructeur automobile Renault, cette thèse vise à fournir des solutions à faible coût énergétique pour la détection non supervisée d'anomalies sur des séries temporelles. Avec l'évolution récente de l'automobile, de plus en plus de données sont produites et doivent être traitées par des algorithmes d'apprentissage automatique. Ce traitement peut être effectué dans le cloud ou directement à bord de la voiture. Dans un tel cas, la bande passante du réseau, les coûts des services cloud, la gestion de la confidentialité des données et la perte de don

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