Academic literature on the topic 'Détection non supervisée d'anomalies'
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Journal articles on the topic "Détection non supervisée d'anomalies":
Truong, Quy Thy, Guillaume Touya, and Cyril de Runz. "Le vandalisme dans l’information géographique volontaireDétection de l’IG volontaire vandalisée." Revue Internationale de Géomatique 29, no. 1 (January 2019): 31–56. http://dx.doi.org/10.3166/rig.2019.00073.
Horner, Holzgreve, Batucan, and Tercanli. "Schwangerschaftsausgang bei 1252 Feten nach Nackentransparenzmessung im ersten Trimenon." Praxis 91, no. 7 (February 1, 2002): 261–65. http://dx.doi.org/10.1024/0369-8394.91.7.261.
Mango, L. J. "Le test Papnet complément du screening conventionnel des frottis vaginaux pour la détection d'anomalies non dépistées." Revue Française des Laboratoires 1995, no. 280 (December 1995): 51–54. http://dx.doi.org/10.1016/s0338-9898(95)80346-7.
Nabeneza, Serge, Vincent Porphyre, and Fabrice Davrieux. "Caractérisation des miels de l’océan Indien par spectrométrie proche infrarouge : étude de faisabilité." Revue d’élevage et de médecine vétérinaire des pays tropicaux 67, no. 3 (June 27, 2015): 130. http://dx.doi.org/10.19182/remvt.10181.
Henni, Khadidja, Olivier Alata, Lynda Zaoui, Abdellatif ELIDRISSI, and Ahmed Moussa. "Classification non Supervisée de Données Multidimensionnelles par les Processus Ponctuels Marqués." Revue Africaine de la Recherche en Informatique et Mathématiques Appliquées Volume 21 - 2015 - Special... (September 3, 2015). http://dx.doi.org/10.46298/arima.2000.
Saddoud, Romain, Kévyn Perlin, Michel Pellat, and Natalia Sergeeva-Chollet. "Développement de l’outil de contrôle in-situ par Courants de Foucault de pièces en cours de Fabrication pour la technique L-PBF." e-journal of nondestructive testing 28, no. 9 (September 2023). http://dx.doi.org/10.58286/28459.
Colpitts, Alexander G. B., and Brent R. Petersen. "Unsupervised Anomaly Detection for Rural Fixed Wireless LTE Networks Détection d’anomalies non supervisée pour les réseaux fixes ruraux sans fil LTE." IEEE Canadian Journal of Electrical and Computer Engineering, 2023, 1–6. http://dx.doi.org/10.1109/icjece.2023.3275975.
Dissertations / Theses on the topic "Détection non supervisée d'anomalies":
Mazel, Johan. "Détection non supervisée d'anomalies dans les réseaux de communication." Phd thesis, INSA de Toulouse, 2011. http://tel.archives-ouvertes.fr/tel-00667654.
Jabiri, Fouad. "Applications de méthodes de classification non supervisées à la détection d'anomalies." Master's thesis, Université Laval, 2020. http://hdl.handle.net/20.500.11794/67914.
In this thesis, we will first present the binary tree partitioning algorithm and isolation forests. Binary trees are very popular classifiers in supervised machine learning. The isolation forest belongs to the family of unsupervised methods. It is an ensemble of binary trees used in common to isolate outlying instances. Subsequently, we will present the approach that we have named "Exponential smoothig" (or "pooling"). This technique consists in encoding sequences of variables of different lengths into a single vector of fixed size. Indeed, the objective of this thesis is to apply the algorithm of isolation forests to identify anomalies in insurance claim forms available in the database of a large Canadian insurance company in order to detect cases of fraud. However, a form is a sequence of claims. Each claim is characterized by a set of variables and thus it will be impossible to apply the isolation forest algorithm directly to this kind of data. It is for this reason that we are going to apply Exponential smoothing. Our application effectively isolates claims and abnormal forms, and we find that the latter tend to be audited by the company more often than regular forms.
Mazel, Johan. "Unsupervised network anomaly detection." Thesis, Toulouse, INSA, 2011. http://www.theses.fr/2011ISAT0024/document.
Anomaly detection has become a vital component of any network in today’s Internet. Ranging from non-malicious unexpected events such as flash-crowds and failures, to network attacks such as denials-of-service and network scans, network traffic anomalies can have serious detrimental effects on the performance and integrity of the network. The continuous arising of new anomalies and attacks create a continuous challenge to cope with events that put the network integrity at risk. Moreover, the inner polymorphic nature of traffic caused, among other things, by a highly changing protocol landscape, complicates anomaly detection system's task. In fact, most network anomaly detection systems proposed so far employ knowledge-dependent techniques, using either misuse detection signature-based detection methods or anomaly detection relying on supervised-learning techniques. However, both approaches present major limitations: the former fails to detect and characterize unknown anomalies (letting the network unprotected for long periods) and the latter requires training over labeled normal traffic, which is a difficult and expensive stage that need to be updated on a regular basis to follow network traffic evolution. Such limitations impose a serious bottleneck to the previously presented problem.We introduce an unsupervised approach to detect and characterize network anomalies, without relying on signatures, statistical training, or labeled traffic, which represents a significant step towards the autonomy of networks. Unsupervised detection is accomplished by means of robust data-clustering techniques, combining Sub-Space clustering with Evidence Accumulation or Inter-Clustering Results Association, to blindly identify anomalies in traffic flows. Correlating the results of several unsupervised detections is also performed to improve detection robustness. The correlation results are further used along other anomaly characteristics to build an anomaly hierarchy in terms of dangerousness. Characterization is then achieved by building efficient filtering rules to describe a detected anomaly. The detection and characterization performances and sensitivities to parameters are evaluated over a substantial subset of the MAWI repository which contains real network traffic traces.Our work shows that unsupervised learning techniques allow anomaly detection systems to isolate anomalous traffic without any previous knowledge. We think that this contribution constitutes a great step towards autonomous network anomaly detection.This PhD thesis has been funded through the ECODE project by the European Commission under the Framework Programme 7. The goal of this project is to develop, implement, and validate experimentally a cognitive routing system that meet the challenges experienced by the Internet in terms of manageability and security, availability and accountability, as well as routing system scalability and quality. The concerned use case inside the ECODE project is network anomaly
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.
In the context of the predictive maintenance of the car manufacturer Renault, this thesis aims at providing low-power solutions for unsupervised anomaly detection on time-series. With the recent evolution of cars, more and more data are produced and need to be processed by machine learning algorithms. This processing can be performed in the cloud or directly at the edge inside the car. In such a case, network bandwidth, cloud services costs, data privacy management and data loss can be saved. Embedding a machine learning model inside a car is challenging as it requires frugal models due to memory and processing constraints. To this aim, we study the usage of spiking neural networks (SNNs) for anomaly detection, prediction and classification on time-series. SNNs models' performance and energy costs are evaluated in an edge scenario using generic hardware models that consider all calculation and memory costs. To leverage as much as possible the sparsity of SNNs, we propose a model with trainable sparse connections that consumes half the energy compared to its non-sparse version. This model is evaluated on anomaly detection public benchmarks, a real use-case of anomaly detection from Renault Alpine cars, weather forecasts and the google speech command dataset. We also compare its performance with other existing SNN and non-spiking models. We conclude that, for some use-cases, spiking models can provide state-of-the-art performance while consuming 2 to 8 times less energy. Yet, further studies should be undertaken to evaluate these models once embedded in a car. Inspired by neuroscience, we argue that other bio-inspired properties such as attention, sparsity, hierarchy or neural assemblies dynamics could be exploited to even get better energy efficiency and performance with spiking models. Finally, we end this thesis with an essay dealing with cognitive neuroscience, philosophy and artificial intelligence. Diving into conceptual difficulties linked to consciousness and considering the deterministic mechanisms of memory, we argue that consciousness and the self could be constitutively independent from memory. The aim of this essay is to question the nature of humans by contrast with the ones of machines and AI
Barreyre, Clementine. "Statistiques en grande dimension pour la détection d'anomalies dans les données fonctionnelles issues des satellites." Thesis, Toulouse, INSA, 2018. http://www.theses.fr/2018ISAT0009/document.
In this PhD, we have developed statistical methods to detect abnormal events in all the functional data produced by the satellite all through its lifecycle. The data we are dealing with come from two main phases in the satellite’s life, telemetries and test data. A first work on this thesis was to understand how to highlight the outliers thanks to projections onto functional bases. On these projections, we have also applied several outlier detection methods, such as the One-Class SVM, the Local Outlier Factor (LOF). In addition to these two methods, we have developed our own outlier detection method, by taking into account the seasonality of the data we consider. Based on this study, we have developed an original procedure to select automatically the most interesting coefficients in a semi-supervised framework for the outlier detection, from a given projection. Our method is a multiple testing procedure where we apply the two sample-test to all the levels of coefficients.We have also chosen to analyze the covariance matrices representing the covariance of the te- lemetries between themselves for the outlier detection in multivariate data. In this purpose, we are comparing the covariance of a cluster of several telemetries deriving from two consecutive days, or consecutive orbit periods. We have applied three statistical tests targeting this same issue with different approaches. We have also developed an original asymptotic test, inspired by both first tests. In addition to the proof of the convergence of this test, we demonstrate thanks to examples that this new test is the most powerful. In this PhD, we have tackled several aspects of the anomaly detection in the functional data deriving from satellites. For each of these methods, we have detected all the major anomalies, improving significantly the false discovery rate
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.
The thesis addresses the challenges of monitoring the environment and detecting anomalies, especially obstacles, for an autonomous freight train. Although traditionally, rail transport was under human supervision, autonomous trains offer potential advantages in terms of costs, time, and safety. However, their operation in complex environments poses significant safety concerns. Instead of a supervised approach that requires costly and limited annotated data, this research adopts an unsupervised technique, using unlabeled data to detect anomalies based on methods capable of identifying atypical behaviors.Two environmental surveillance models are presented : the first, based on a convolutional autoencoder (CAE), is dedicated to identifying obstacles on the main track; the second, an advanced version incorporating the vision transformer (ViT), focuses on overall environmental surveillance. Both employ unsupervised learning techniques for anomaly detection.The results show that the highlighted method offers relevant insights for monitoring the environment of the autonomous freight train, holding potential to enhance its reliability and safety. The use of unsupervised techniques thus showcases the utility and relevance of their adoption in an application context for the autonomous train
Truong, Thi Bich Thanh. "Home Automation Monitoring for Assisted Living Services and Healthcare." Lorient, 2010. http://www.theses.fr/2010LORIS204.
With the development of technology and information, there are more and more opportunities and challenges for healthcare and assistance services for disabled people as well as the elderly. In this context, this PhD work proposes and demonstrates a new solution for home monitoring. Our approach is based on the idea that existing home automation and multimedia services provide some relevant information to be used as available sensors for remote monitoring. Through the analysis of user habits, our work includes two steps. In the first step, we automate a scenario identification, based on a combination of data mining, AI, graph theory and operational research algorithms to offer scenarios self adapting to user capabilities, while facilitating user access to the services. In the second step, this sensor information is used for alert management based on the anomaly detection, meaning a deviation of usual habits. These two steps provide a low level and non-intrusive personal monitoring while giving people more autonomy and confidence in their environments. A simulation model is developed in a first stage for the generation of user database without waiting for months monitoring user activities. This simulation data allows us to develop, tune and evaluate different aspects of our approach, before being applied in a real context. Then an experimentation through the IR recording is realized to monitor the user activities. The results of these real data allow us to evaluate the performance as well as the efficiency of our solution
Pantin, Jérémie. "Détection et caractérisation sémantique de données textuelles aberrantes." Electronic Thesis or Diss., Sorbonne université, 2023. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2023SORUS347.pdf.
Machine learning answers to the problem of handling dedicated tasks with a wide variety of data. Such algorithms can be either simple or difficult to handle depending of the data. Low dimensional data (2-dimension or 3-dimension) with an intuitive representation (average of baguette price by years) are easier to interpret/explain for a human than data with thousands of dimensions. For low dimensional data, the error leads to a significant shift against normal data, but for the case of high dimensional data it is different. Outlier detection (or anomaly detection, or novelty detection) is the study of outlying observations for detecting what is normal and abnormal. Methods that perform such task are algorithms, methods or models that are based on data distributions. Different families of approaches can be found in the literature of outlier detection, and they are mainly independent of ground truth. They perform outlier analysis by detecting the principal behaviors of majority of observations. Thus, data that differ from normal distribution are considered noise or outlier. We detail the application of outlier detection with text. Despite recent progress in natural language processing, computer still lack profound understanding of human language in absence of information. For instance, the sentence "A smile is a curve that set everything straight" has several levels of understanding and a machine can encounter hardship to chose the right level of lecture. This thesis presents the analysis of high-dimensional outliers, applied to text. Recent advances in anomaly detection and outlier detection are not significantly represented with text data and we propose to highlight the main differences with high-dimensional outliers. We also approach ensemble methods that are nearly nonexistent in the literature for our context. Finally, an application of outlier detection for elevate results on abstractive summarization is conducted. We propose GenTO, a method that prepares and generates split of data in which anomalies and outliers are inserted. Based on this method, evaluation and benchmark of outlier detection approaches is proposed with documents. The proposed taxonomy allow to identify difficult and hierarchised outliers that the literature tackles without knowing. Also, learning without supervision often leads models to rely in some hyperparameter. For instance, Local Outlier Factor relies to the k-nearest neighbors for computing the local density. Thus, choosing the right value for k is crucial. In this regard, we explore the influence of such parameter for text data. While choosing one model can leads to obvious bias against real-world data, ensemble methods allow to mitigate such problem. They are particularly efficient with outlier analysis. Indeed, the selection of several values for one hyperparameter can help to detect strong outliers.Importance is then tackled and can help a human to understand the output of black box model. Thus, the interpretability of outlier detection models is questioned. We find that for numerous dataset, a low number of features can be selected as oracle. The association of complete models and restrained models helps to mitigate the black-box effect of some approaches. In some cases, outlier detection refers to noise removal or anomaly detection. Some applications can benefit from the characteristic of such task. Mail spam detection and fake news detection are one example, but we propose to use outlier detection approaches for weak signal exploration in marketing project. Thus, we find that the model of the literature help to improve unsupervised abstractive summarization, and also to find weak signals in text
Audibert, Julien. "Unsupervised anomaly detection in time-series." Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS358.
Anomaly detection in multivariate time series is a major issue in many fields. The increasing complexity of systems and the explosion of the amount of data have made its automation indispensable. This thesis proposes an unsupervised method for anomaly detection in multivariate time series called USAD. However, deep neural network methods suffer from a limitation in their ability to extract features from the data since they only rely on local information. To improve the performance of these methods, this thesis presents a feature engineering strategy that introduces non-local information. Finally, this thesis proposes a comparison of sixteen time series anomaly detection methods to understand whether the explosion in complexity of neural network methods proposed in the current literature is really necessary
Attal, Ferhat. "Classification de situations de conduite et détection des événements critiques d'un deux roues motorisé." Thesis, Paris Est, 2015. http://www.theses.fr/2015PEST1003/document.
This thesis aims to develop framework tools for analyzing and understanding the riding of Powered Two Wheelers (PTW). Experiments are conducted using instrumented PTW in real context including both normal (naturalistic) riding behaviors and critical riding behaviors (near fall and fall). The two objectives of this thesis are the riding patterns classification and critical riding events detection. In the first part of this thesis, a machine-learning framework is used for riding pattern recognition problem. Therefore, this problem is formulated as a classification task to identify the class of riding patterns. The approaches developed in this context have shown the interest to take into account the temporal aspect of the data in PTW riding. Moreover, we have shown the effectiveness of hidden Markov models for such problem. The second part of this thesis focuses on the development of the off-line detection and classification of critical riding events tools and the on-line fall detection. The problem of detection and classification of critical riding events has been performed towards two steps: (1) the segmentation step, where the multidimensional time of data were modeled and segmented by using a mixture model with quadratic logistic proportions; (2) the classification step, which consists in using a pattern recognition algorithm in order to assign each event by its extracted features to one of the three classes namely Fall, near Fall and Naturalistic riding. Regarding the fall detection problem, it is formulated as a sequential anomaly detection problem. The Multivariate CUmulative SUM (MCUSUM) control chart was applied on the data collected from sensors mounted on the motorcycle. The obtained results on a real database have shown the effectiveness of the proposed methodology for both riding pattern recognition and critical riding events detection problems
Book chapters on the topic "Détection non supervisée d'anomalies":
ATTO, Abdourrahmane M., Fatima KARBOU, Sophie GIFFARD-ROISIN, and Lionel BOMBRUN. "Clustering fonctionnel de séries d’images par entropies relatives." In Détection de changements et analyse des séries temporelles d’images 1, 121–38. ISTE Group, 2022. http://dx.doi.org/10.51926/iste.9056.ch4.
LIU, Sicong, Francesca BOVOLO, Lorenzo BRUZZONE, Qian DU, and Xiaohua TONG. "Détection non supervisée des changements dans des images multitemporelles." In Détection de changements et analyse des séries temporelles d’images 1, 5–40. ISTE Group, 2022. http://dx.doi.org/10.51926/iste.9056.ch1.