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Dissertations / Theses on the topic 'Anomaly Detection'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Ståhl, Björn. "Online Anomaly Detection." Thesis, Blekinge Tekniska Högskola, Avdelningen för för interaktion och systemdesign, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2825.

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Where the role of software-intensive systems has shifted from the traditional one of fulfilling isolated computational tasks, larger collaborative societies with interaction as primary resource, is gradually taking its place. This can be observed in anything from logistics to rescue operations and resource management, numerous services with key-roles in the modern infrastructure. In the light of this new collaborative order, it is imperative that the tools (compilers, debuggers, profilers) and methods (requirements, design, implementation, testing) that supported traditional software engineeri

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2

Tran, Thi Minh Hanh. "Anomaly detection in video." Thesis, University of Leeds, 2018. http://etheses.whiterose.ac.uk/22443/.

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Anomaly detection is an area of video analysis that has great importance in automated surveillance. Although it has been extensively studied, there has been little work on using deep convolutional neural networks to learn spatio-temporal feature representations. In this thesis we present novel approaches for learning motion features and modelling normal spatio-temporal dynamics for anomaly detection. The contributions are divided into two main chapters. The ﬁrst introduces a method that uses a convolutional autoencoder to learn motion features from foreground optical ﬂow patches. The autoencod

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3

Barone, Joshua M. "Automated Timeline Anomaly Detection." ScholarWorks@UNO, 2013. http://scholarworks.uno.edu/td/1609.

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Digital forensics is the practice of trained investigators gathering and analyzing evidence from digital devices such as computers and smart phones. On these digital devices, it is possible to change the time on the device for a purpose other than what is intended. Currently there are no documented techniques to determine when this occurs. This research seeks to prove out a technique for determining when the time has been changed on forensic disk image by analyzing the log files found on the image. Out of this research a tool is created to perform this analysis in automated fashion. This tool

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4

Samuelsson, Jonas. "Anomaly Detection in ConsoleLogs." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-314514.

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The overall purpose of this project was to find anomalies inunstructured console logs. Logs were generated from system componentsin a contact center, specifically components in an email chain. Ananomaly is behaviour that can be described as abnormal. Suchbehaviour was found by creating features of the data that later oncould be analyzed by a data mining model. The mining model involvedthe usage of normalisation methods together with different distancefunctions. The algorithms that were used in order to generate resultson the prepared data were DBSCAN, Local Outlier Factor, and k-NNGlobal Anoma

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Das, Mahashweta. "Spatio-Temporal Anomaly Detection." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1261540196.

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6

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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7

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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8

Nordlöf, Jonas. "Anomaly detection in videosurveillance feeds." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-105521.

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Traditional passive surveillance is proving ineffective as the number of available cameras for an operator often exceeds the operators ability to monitor them. Furthermore, monitoring surveillance cameras requires a focus that operators can only uphold for a short amount of time. Algorithms for automatic detection of anomalies in video surveillance feeds are thereby constructed and presented in this thesis by using hidden Markov models (HMM) and a Gaussian mixture probability hypothesis density (GM-PHD) filter. Four different models are created and evaluated using the PETS2009 dataset and a si

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9

Turcotte, Melissa. "Anomaly detection in dynamic networks." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/24673.

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Anomaly detection in dynamic communication networks has many important security applications. These networks can be extremely large and so detecting any changes in their structure can be computationally challenging; hence, computationally fast, parallelisable methods for monitoring the network are paramount. For this reason the methods presented here use independent node and edge based models to detect locally anomalous substructures within communication networks. As a first stage, the aim is to detect changes in the data streams arising from node or edge communications. Throughout the thesis

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10

Zhang, Dongyang. "PRAAG Algorithm in Anomaly Detection." Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-194193.

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Anomaly detection has been one of the most important applications of datamining, widely applied in industries like financial, medical,telecommunication, even manufacturing. In many scenarios, data are in theform of streaming in a large amount, so it is preferred to analyze the datawithout storing all of them. In other words, the key is to improve the spaceefficiency of algorithms, for example, by extracting the statistical summary ofthe data. In this thesis, we study the PRAAG algorithm, a collective anomalydetection algorithm based on quantile feature of the data, so the spaceefficiency essen

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11

Ohlsson, Jonathan. "Anomaly Detection in Microservice Infrastructures." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231993.

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Anomaly detection in time series is a broad field with many application areas, and has been researched for many years. In recent years the need for monitoring and DevOps has increased, partly due to the increased usage of microservice infrastructures. Applying time series anomaly detection to the metrics emitted by these microservices can yield new insights into the system health and could enable detecting anomalous conditions before they are escalated into a full incident. This thesis investigates how two proposed anomaly detectors, one based on the RPCA algorithm and the other on the HTM neu

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12

Aradhye, Hrishikesh Balkrishna. "Anomaly Detection Using Multiscale Methods." The Ohio State University, 2001. http://rave.ohiolink.edu/etdc/view?acc_num=osu989701610.

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13

Ioannidou, Polyxeni. "Anomaly Detection in Computer Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-295762.

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In this degree project, we study the anomaly detection problem in log files of computer networks. In particular, we try to find an efficient way to detect anomalies in our data, which consist of different logging messages from different systems in CERN’s network for the LHC-b experiment. The contributions of the thesis are double: 1) The thesis serves as a survey on how we can detect threats, and errors in systems that are logging a huge amount of messages in the databases of a computer network. 2) Scientists in the LHC-b experiment make use of the Elasticsearch, which is an open source search

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14

Patton, Michael Dean. "Seedlet Technology for anomaly detection." Diss., Mississippi State : Mississippi State University, 2002. http://library.msstate.edu/etd/show.asp?etd=etd-08022002-142101.

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15

Alkadi, Alaa. "Anomaly Detection in RFID Networks." UNF Digital Commons, 2017. https://digitalcommons.unf.edu/etd/768.

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Available security standards for RFID networks (e.g. ISO/IEC 29167) are designed to secure individual tag-reader sessions and do not protect against active attacks that could also compromise the system as a whole (e.g. tag cloning or replay attacks). Proper traffic characterization models of the communication within an RFID network can lead to better understanding of operation under “normal” system state conditions and can consequently help identify security breaches not addressed by current standards. This study of RFID traffic characterization considers two piecewise-constant data smoothing

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16

Aussel, Nicolas. "Real-time anomaly detection with in-flight data : streaming anomaly detection with heterogeneous communicating agents." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLL007/document.

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Avec l'augmentation du nombre de capteurs et d'actuateurs dans les avions et le développement de liaisons de données fiables entre les avions et le sol, il est devenu possible d'améliorer la sécurité et la fiabilité des systèmes à bord en appliquant des techniques d'analyse en temps réel. Cependant, étant donné la disponibilité limité des ressources de calcul embarquées et le coût élevé des liaisons de données, les solutions architecturelles actuelles ne peuvent pas exploiter pleinement toutes les ressources disponibles, limitant leur précision.Notre but est de proposer un algorithme distribué

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Aussel, Nicolas. "Real-time anomaly detection with in-flight data : streaming anomaly detection with heterogeneous communicating agents." Electronic Thesis or Diss., Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLL007.

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Avec l'augmentation du nombre de capteurs et d'actuateurs dans les avions et le développement de liaisons de données fiables entre les avions et le sol, il est devenu possible d'améliorer la sécurité et la fiabilité des systèmes à bord en appliquant des techniques d'analyse en temps réel. Cependant, étant donné la disponibilité limité des ressources de calcul embarquées et le coût élevé des liaisons de données, les solutions architecturelles actuelles ne peuvent pas exploiter pleinement toutes les ressources disponibles, limitant leur précision.Notre but est de proposer un algorithme distribué

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18

Laxhammar, Rikard. "Conformal anomaly detection : Detecting abnormal trajectories in surveillance applications." Doctoral thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-8762.

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Human operators of modern surveillance systems are confronted with an increasing amount of trajectory data from moving objects, such as people, vehicles, vessels, and aircraft. A large majority of these trajectories reflect routine traffic and are uninteresting. Nevertheless, some objects are engaged in dangerous, illegal or otherwise interesting activities, which may manifest themselves as unusual and abnormal trajectories. These anomalous trajectories can be difficult to detect by human operators due to cognitive limitations. In this thesis, we study algorithms for the automated detection of

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19

Olsson, Jonathan. "Detecting Faulty Piles of Wood using Anomaly Detection Techniques." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-83061.

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The forestry and the sawmill industry have a lot of incoming and outgoing piles of wood. It's important to maintain quality and efficiency. This motivates an examination of whether machine learning- or more specifically, anomaly detection techniques can be implemented and used to detect faulty shipments. This thesis presents and evaluates some computer vision techniques and some deep learning techniques. Deep learning can be divided into groups; supervised, semi-supervised and unsupervised. In this thesis, all three groups were examined and it covers supervised methods such as Convolutional Ne

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20

Huang, Chengqiang. "Featured anomaly detection methods and applications." Thesis, University of Exeter, 2018. http://hdl.handle.net/10871/34351.

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Anomaly detection is a fundamental research topic that has been widely investigated. From critical industrial systems, e.g., network intrusion detection systems, to people’s daily activities, e.g., mobile fraud detection, anomaly detection has become the very first vital resort to protect and secure public and personal properties. Although anomaly detection methods have been under consistent development over the years, the explosive growth of data volume and the continued dramatic variation of data patterns pose great challenges on the anomaly detection systems and are fuelling the great deman

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21

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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22

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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23

Brauckhoff, Daniela. "Network traffic anomaly detection and evaluation." Aachen Shaker, 2010. http://d-nb.info/1001177746/04.

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24

Brax, Christoffer. "Anomaly detection in the surveillance domain." Doctoral thesis, Örebro universitet, Akademin för naturvetenskap och teknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-16373.

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In the post September 11 era, the demand for security has increased in virtually all parts of the society. The need for increased security originates from the emergence of new threats which differ from the traditional ones in such a way that they cannot be easily defined and are sometimes unknown or hidden in the “noise” of daily life. When the threats are known and definable, methods based on situation recognition can be used find them. However, when the threats are hard or impossible to define, other approaches must be used. One such approach is data-driven anomaly detection, where a model o

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Riveiro, María José. "Visual analytics for maritime anomaly detection." Doctoral thesis, Örebro universitet, Akademin för naturvetenskap och teknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-12783.

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The surveillance of large sea areas typically involves the analysis of huge quantities of heterogeneous data. In order to support the operator while monitoring maritime traffic, the identification of anomalous behavior or situations that might need further investigation may reduce operators' cognitive load. While it is worth acknowledging that existing mining applications support the identification of anomalies, autonomous anomaly detection systems are rarely used for maritime surveillance. Anomaly detection is normally a complex task that can hardly be solved by using purely visual or purel

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Satam, Shalaka Chittaranjan, and Shalaka Chittaranjan Satam. "Bluetooth Anomaly Based Intrusion Detection System." Thesis, The University of Arizona, 2017. http://hdl.handle.net/10150/625890.

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Bluetooth is a wireless technology that is used to communicate over personal area networks (PAN). With the advent of Internet of Things (IOT), Bluetooth is the technology of choice for small and short range communication networks. For instance, most of the modern cars have the capability to connect to mobile devices using Bluetooth. This ubiquitous presence of Bluetooth makes it important that it is secure and its data is protected. Previous work has shown that Bluetooth is vulnerable to attacks like the man in the middle attack, Denial of Service (DoS) attack, etc. Moreover, all Bluetooth dev

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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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29

Tziakos, Ioannis. "Subspace discovery for video anomaly detection." Thesis, Queen Mary, University of London, 2010. http://qmro.qmul.ac.uk/xmlui/handle/123456789/387.

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In automated video surveillance anomaly detection is a challenging task. We address this task as a novelty detection problem where pattern description is limited and labelling information is available only for a small sample of normal instances. Classification under these conditions is prone to over-fitting. The contribution of this work is to propose a novel video abnormality detection method that does not need object detection and tracking. The method is based on subspace learning to discover a subspace where abnormality detection is easier to perform, without the need of detailed annotation

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30

Pellissier, Muriel. "Anomaly detection technique for sequential data." Thesis, Grenoble, 2013. http://www.theses.fr/2013GRENM078/document.

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De nos jours, beaucoup de données peuvent être facilement accessibles. Mais toutes ces données ne sont pas utiles si nous ne savons pas les traiter efficacement et si nous ne savons pas extraire facilement les informations pertinentes à partir d'une grande quantité de données. Les techniques de détection d'anomalies sont utilisées par de nombreux domaines afin de traiter automatiquement les données. Les techniques de détection d'anomalies dépendent du domaine d'application, des données utilisées ainsi que du type d'anomalie à détecter.Pour cette étude nous nous intéressons seulement aux donnée

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31

Udd, Robert. "Anomaly Detection in SCADA Network Traffic." Thesis, Linköpings universitet, Programvara och system, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-122680.

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Critical infrastructure provides us with the most important parts of modern society, electricity, water and transport. To increase efficiency and to meet new demands from the customer remote monitoring and control of the systems is necessary. This opens new ways for an attacker to reach the Supervisory Control And Data Acquisition (SCADA) systems that control and monitors the physical processes involved. This also increases the need for security features specially designed for these settings. Anomaly-based detection is a technique suitable for the more deterministic SCADA systems. This thesis

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Svensson, Carolin. "Anomaly Detection in Encrypted WLAN Traffic." Thesis, Linköpings universitet, Kommunikationssystem, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-172689.

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33

Jirwe, Marcus. "Online Anomaly Detection on the Edge." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-299565.

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The society of today relies a lot on the industry and the automation of factory tasks is more prevalent than ever before. However, the machines taking on these tasks require maintenance to continue operating. This maintenance is typically given periodically and can be expensive while sometimes requiring expert knowledge. Thus it would be very beneficial if one could predict when a machine needs maintenance and only employ maintenance as necessary. One method to predict when maintenance is necessary is to collect sensor data from a machine and analyse it for anomalies. Anomalies are usually an

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Wu, Mingxi. "Statistical methods for fast anomaly detection." [Gainesville, Fla.] : University of Florida, 2008. http://purl.fcla.edu/fcla/etd/UFE0022572.

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Rosario, Dalton S. "Algorithm development for hyperspectral anomaly detection." College Park, Md.: University of Maryland, 2008. http://hdl.handle.net/1903/8583.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2008.<br>Thesis research directed by: Applied Mathematics and Scientific Computation Program. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

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GHORBANI, SONIYA. "Anomaly Detection in Electricity Consumption Data." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-35011.

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Distribution grids play an important role in delivering electricityto end users. Electricity customers would like to have a continuouselectricity supply without any disturbance. For customerssuch as airports and hospitals electricity interruption may havedevastating consequences. Therefore, many electricity distributioncompanies are looking for ways to prevent power outages.Sometimes the power outages are caused from the grid sidesuch as failure in transformers or a break down in power cablesbecause of wind. And sometimes the outages are caused bythe customers such as overload. In fact, a very

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Oriwoh, Edewede. "A smart home anomaly detection framework." Thesis, University of Bedfordshire, 2015. http://hdl.handle.net/10547/622486.

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Smart Homes (SHs), as subsets of the Internet of Things (IoT), make use of Machine Learning and Arti cial Intelligence tools to provide technology-enabled solutions which assist their occupants and users with their Activities of Daily Living (ADL). Some SH provide always-present, health management support and care services. Having these services provided at home enables SH occupants such as the elderly and disabled to continue to live in their own homes and localities thus aiding Ageing In Place goals and eliminating the need for them to be relocated in order to be able to continue receiving t

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Rossell, Daniel. "Anomaly detection using adaptive resonance theory." Thesis, Boston University, 2013. https://hdl.handle.net/2144/12205.

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Thesis (M.S.)--Boston University<br>This thesis focuses on the problem of anomaly detection in computer networks. Anomalies are often malicious intrusion attempts that represent a serious threat to network security. Adaptive Resonance Theory (ART) is used as a classification scheme for identifying malicious network traffic. ART was originally developed as a theory to explain how the human eye categorizes visual patterns. For network intrusion detection, the core ART algorithm is implemented as a clustering algorithm that groups network traffic into clusters. A machine learning process allows t

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Örneholm, Filip. "Anomaly Detection in Seasonal ARIMA Models." Thesis, Uppsala universitet, Tillämpad matematik och statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-388503.

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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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Abdelaty, Maged Fathy Youssef. "Robust Anomaly Detection in Critical Infrastructure." Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/352463.

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Critical Infrastructures (CIs) such as water treatment plants, power grids and telecommunication networks are critical to the daily activities and well-being of our society. Disruption of such CIs would have catastrophic consequences for public safety and the national economy. Hence, these infrastructures have become major targets in the upsurge of cyberattacks. Defending against such attacks often depends on an arsenal of cyber-defence tools, including Machine Learning (ML)-based Anomaly Detection Systems (ADSs). These detection systems use ML models to learn the profile of the normal behavio

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Dao, Quang Hoan <1992&gt. "Anomaly detection with time series forecasting." Master's Degree Thesis, Università Ca' Foscari Venezia, 2020. http://hdl.handle.net/10579/17320.

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Anomaly detection time series is a very large and complex field. In recent years, several techniques based on data science were designed in order to improve the efficiency of methods developed for this purpose. In this paper, we introduce Recurrent Neural Networks (RNNs) with LSTM units, ARIMA and Facebook Prophet library for dectecting the anomalies with time series forcasting. Due to the challenges in obtaining labeled anomaly datasets, an unsupervised approach is employed. Unsupervised anomaly detection is the process of finding outlying records in a given dataset without prior need for tra

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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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Staerman, Guillaume. "Functional anomaly detection and robust estimation." Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAT021.

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L’engouement pour l’apprentissage automatique s’étend à presque tous les domaines comme l’énergie, la médecine ou la finance. L’omniprésence des capteurs met à disposition de plus en plus de données avec une granularité toujours plus fine. Une abondance de nouvelles applications telles que la surveillance d’infrastructures complexes comme les avions ou les réseaux d’énergie, ainsi que la disponibilité d’échantillons de données massives, potentiellement corrompues, ont mis la pression sur la communauté scientifique pour développer de nouvelles méthodes et algorithmes d’apprentissage automatique

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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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Balocchi, Leonardo. "Anomaly detection mediante algoritmi di machine learning." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2019.

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L'obiettivo dell'elaborato è quello di studiare lo stato di danneggiamento del ponte Z-24, struttura che in fase di demolizione è stata sottoposta ad un danneggiamento progressivo. Lo studio è stato effettuato tramite algoritmi di machine learning ed in particolare sono stati scelti due algoritmi di anomaly detection. Dato che gli algoritmi utilizzati sono due, più chiaramente ACH e KNN, alla fine dell'elaborato è stato effettuato un confronto per capire quale fosse il migliore per l'analisi strutturale. Il confronto fra i due algoritmi è stato fatto tramite matrici di confusione e tramite

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SUNDHOLM, JOEL. "Feature Extraction for Anomaly Detection inMaritime Trajectories." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-155898.

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The operators of a maritime surveillance system are hardpressed to make complete use of the near real-time informationflow available today. To assist them in this matterthere has been an increasing amount of interest in automated systems for the detection of anomalous trajectories.Specifically, it has been proposed that the framework of conformal anomaly detection can be used, as it provides the key property of a well-tuned alarm rate. However, inorder to get an acceptable precision there is a need to carefully tailor the nonconformity measure used to determine if a trajectory is anomalous. Th

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Balupari, Ravindra. "Real-time network-based anomaly intrusion detection." Ohio : Ohio University, 2002. http://www.ohiolink.edu/etd/view.cgi?ohiou1174579398.

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Eriksson, André. "Anomaly Detection inMachine-Generated Data:A Structured Approach." Thesis, KTH, Matematisk statistik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-121549.

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Anomaly detection is an important issue in data mining and analysis, with applications in almost every area in science, technology and business that involves data collection. The development of general anomaly detection techniques can therefore have a large impact on data analysis across many domains. In spite of this, little work has been done to consolidate the different approaches to the subject. In this report, this deficiency is addressed in the target domain of temporal machine-generated data. To this end, new theory for comparing and reasoning about anomaly detection tasks and methods i

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Buthpitiya, Senaka. "Modeling Mobile User Behavior for Anomaly Detection." Research Showcase @ CMU, 2014. http://repository.cmu.edu/dissertations/362.

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As ubiquitous computing (ubicomp) technologies reach maturity, smart phones and context-based services are gaining mainstream popularity. A smart phone accompanies its user throughout (nearly) all aspects of his life, becoming an indispensable assistant the busy user relies on to help navigate his life, using map applications to navigate the physical world, email and instant messaging applications to keep in touch, media player applications to be entertained, etc. As a smart phone is capable of sensing the physical and virtual context of the user with an array of “hard” sensors (e.g., GPS, acc

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