An autonomic traffic analysis proposal using Machine Learning techniques

International audienceNetwork analysis has recently become in one of the most challenging tasks to handle due to the rapid growth of communication technologies. For network management, accurate identification and classification of network traffic is a key task. For example, identifying traffic from different applications is critical to manage bandwidth resources and to ensure Quality of Service objectives. Machine learning emerges as a suitable tool for traffic classification; however, it requires several steps that must be followed adequately in order to achieve the goals. In this paper, we proposed an architecture to perform traffic analysis based on Machine Learning techniques and autonomic computing. We analyze the procedures to perform Machine Learning over traffic network classification, and at the same time we give guidelines to introduce all these procedures into the architecture proposed. The main contribution of our proposal is the reconfiguration of the traffic classifier that will change according to the knowledge adquired from the traffic analysis process

A novel statistical based feature extraction approach for the inner-class feature estimation using linear regression

International audienceNowadays, statistical based feature extraction approaches are commonly used in the knowledge discovery field with Machine Learning. These features are accurate and give relevant information of the samples; however, these approaches consider some assumptions, such as the membership of the signals or samples to specific statistical distributions. In this work, we propose to model statistical computation through Linear Regression (LR) models; these models will be divided by classes, in order to increase the inner-class identification likelihood. In general, an ensemble of LR models will estimate a targeted statistical feature. In an online deployment, the pool of LR models of a given targeted statistical feature will be evaluated to find the most similar value to the current input, which will be as the estimated of the feature. The proposal is tested with a real world application in traffic network classification. In this case study, fast classification response has to be provided, and statistical based features are widely used for this aim. In this sense, the statistical features must give early signs about the status of the network in order to achieve some objectives such as improve the quality of service or detect malicious traffic

Observer-biased bearing condition monitoring: from fault detection to multi-fault classification

Bearings are simultaneously a fundamental component and one of the principal causes of failure in rotary machinery. The work focuses on the employment of fuzzy clustering for bearing condition monitoring, i.e., fault detection and classification. The output of a clustering algorithm is a data partition (a set of clusters) which is merely a hypothesis on the structure of the data. This hypothesis requires validation by domain experts. In general, clustering algorithms allow a limited usage of domain knowledge on the cluster formation process. In this study, a novel method allowing for interactive clustering in bearing fault diagnosis is proposed. The method resorts to shrinkage to generalize an otherwise unbiased clustering algorithm into a biased one. In this way, the method provides a natural and intuitive way to control the cluster formation process, allowing for the employment of domain knowledge to guiding it. The domain expert can select a desirable level of granularity ranging from fault detection to classification of a variable number of faults and can select a specific region of the feature space for detailed analysis. Moreover, experimental results under realistic conditions show that the adopted algorithm outperforms the corresponding unbiased algorithm (fuzzy c-means) which is being widely used in this type of problems. (C) 2016 Elsevier Ltd. All rights reserved.Grant number: 145602

Towards the Deployment of Machine Learning Solutions in Network Traffic Classification: A Systematic Survey

International audienceTraffic analysis is a compound of strategies intended to find relationships, patterns, anomalies, and misconfigurations, among others things, in Internet traffic. In particular, traffic classification is a subgroup of strategies in this field that aims at identifying the application's name or type of Internet traffic. Nowadays, traffic classification has become a challenging task due to the rise of new technologies, such as traffic encryption and encapsulation, which decrease the performance of classical traffic classification strategies. Machine Learning gains interest as a new direction in this field, showing signs of future success, such as knowledge extraction from encrypted traffic, and more accurate Quality of Service management. Machine Learning is fast becoming a key tool to build traffic classification solutions in real network traffic scenarios; in this sense, the purpose of this investigation is to explore the elements that allow this technique to work in the traffic classification field. Therefore, a systematic review is introduced based on the steps to achieve traffic classification by using Machine Learning techniques. The main aim is to understand and to identify the procedures followed by the existing works to achieve their goals. As a result, this survey paper finds a set of trends derived from the analysis performed on this domain; in this manner, the authors expect to outline future directions for Machine Learning based traffic classification

Techniques de classification pour la gestion de la « qualité de service » dans les systèmes de communication par satellite

The Internet has become indispensable for the daily activities of human beings. Nowadays, this network system serves as a platform for communication, transaction, and entertainment, among others. This communication system is characterized by terrestrial and Satellite components that interact between themselves to provide transmission paths of information between endpoints. Particularly, Satellite Communication providers’ interest is to improve customer satisfaction by optimally exploiting on demand available resources and offering Quality of Service (QoS). Improving the QoS implies to reduce errors linked to information loss and delays of Internet packets in Satellite Communications. In this sense, according to Internet traffic (Streaming, VoIP, Browsing, etc.) and those error conditions, the Internet flows can be classified into different sensitive and non-sensitive classes. Following this idea, this thesis project aims at finding new Internet traffic classification approaches to improving customer satisfaction by improving the QoS.Machine Learning (ML) algorithms will be studied and deployed to classify Internet traffic. All the necessary elements, to couple an ML solution over a well-known Satellite Communication and QoS management architecture, will be evaluated. In this architecture, one or more monitoring points will intercept Satellite Internet traffic, which in turn will be treated and marked with predefined classes by ML-based classification techniques. The marked traffic will be interpreted by a QoS management architecture that will take actions according to the class type.To develop this ML-based solution, a rich and complete set of Internet traffic is required; however, historical labeled data is hardly publicly available. In this context, binary packets should be monitored and stored to generate historical data. To do so, an emulated cloud platform will serve as a data generation environment in which different Internet communications will be launched and captured. This study is escalated to a Satellite Communication architecture. Moreover, statistical-based features are extracted from the packet flows. Some statistical-based computations will be adapted to achieve accurate Internet traffic classification for encrypted and unencrypted packets in the historical data. Afterward, a proposed classification system will deal with different Internet communications (encrypted, unencrypted, and tunneled). This system will process the incoming traffic hierarchically to achieve a high classification performance. Besides, to cope with the evolution of Internet applications, a new method is presented to induce updates over the original classification system. Finally, some experiments in the cloud emulated platform validate our proposal and set guidelines for its deployment over a Satellite architecture.De nos jours, Internet est devenu indispensable dans le quotidien des humains. Aujourd’hui, ce réseau sert entre autres de plate-forme de communication, de système transactions et de divertissement. Ces activités sont possibles grâce à des composants satellites qui gèrent les flux d'informations. Dans ce contexte, l'intérêt des fournisseurs de communications satellites est d'améliorer la satisfaction des clients à travers d'utilisation optimale de ces ressources. La qualité de service (QDS) est utilisée pour accomplir cet objectif. Améliorer la QDS permet la réduction des erreurs liées à la perte et la latence paquets; par conséquent “la qualité de service” (QDS) aide à optimiser le trafic internet. En fonction du trafic internet (Streaming, VoIP, Transfert de fichiers, etc.) et de ses erreurs, le flux de paquet peut être classé parmi plusieurs catégories. En suivant cette idée, ce projet de thèse vise à trouver des nouvelles approches de classification du trafic Internet pour améliorer la QDS.Pour classifier le trafic Internet, l'apprentissage automatique sera étudié et déployé. Les composants qui permettront de coupler une solution d'apprentissage automatique avec une architecture satellite et de qualité de service seront évalués. Dans cette architecture, un ou plusieurs points de surveillance capteront le trafic Internet. Des techniques de classifications marqueront le trafic capté en classes qui seront interprétées par l'architecture de la qualité de service.Pour développer notre solution, une base de données riche et complète sera requise; toutefois, les données historiques labellisées sont difficilement disponibles pour le public. Dans ce contexte, des paquets binaires seront extraits et stockées pour générer un historique de données. Par conséquent, une plate-forme d'émulation du trafic Internet sur le cloud pour générer des flux de communication a été proposée. Cela sera aussi implanté sur une plateforme d'émulation de communication Satellite. En outre, des flux IP devront être construits avec les paquets et quelques caractéristiques statistiques pour discriminer et décrire le trafic Internet correctement seront présentées. Ensuite, un système de classification sera capable de gérer différentes communications sur Internet (cryptées, non cryptées et en tunnel). Ce système traitera le trafic entrant de manière hiérarchique pour atteindre une performance de classification élevée. Par ailleurs, pour faire face à l'évolution des applications Internet, une nouvelle méthode est présentée pour induire des mises à jour au système de classification initiale. Finalement, des expériences sur la plate-forme émulée dans le cloud seront mises en place pour valider notre proposition et définir des directives pour son déploiement sur l’architecture Satellite

A framework to classify heterogeneous Internet traffic with Machine Learning and Deep Learning techniques for satellite communications

International audienceNowadays, the Internet network system serves as a platform for communication, transaction, and entertainment, among others. This communication system is characterized by terrestrial and Satellite components that interact between themselves to provide transmission paths of information between endpoints. Particularly, Satellite Communication providers' interest is to improve customer satisfaction by optimally exploiting on demand available resources and offering Quality of Service (QoS). Improving the QoS implies to reduce errors linked to information loss and delays of Internet packets in Satellite Communications. In this sense, according to Internet traffic (Streaming, VoIP, Browsing, etc.) and those error conditions, the Internet flows can be classified into different sensitive and non-sensitive classes. Following this idea, this work aims at finding new Internet traffic classification approaches to improving the QoS. Machine Learning (ML) and Deep Learning (DL) techniques will be studied and deployed to classify Internet traffic. All the necessary elements to couple an ML or DL solution over a well-known Satellite Communication and QoS management architecture will be evaluated. To develop this solution, a rich and complete set of Internet traffic is required. In this context, an emulated Satellite Communication platform will serve as a data generation environment in which different Internet communications will be launched and captured. The proposed classification system will deal with different Internet communications (encrypted, unencrypted, and tunneled). This system will process the incoming traffic hierarchically to achieve a high classification performance. Finally, some experiments on a cloud emulated platform validates our proposal and set guidelines for its deployment over a Satellite architecture

Automatic feature extraction of time-series applied to fault severity assessment of helical gearbox in stationary and non-stationary speed operation

Signals captured in rotating machines to obtain the status of their components can be considered as a source of massive information. In current methods based on artificial intelligence to fault severity assessment, features are first generated by advanced signal processing techniques. Then feature selection takes place, often requiring human expertise. This approach, besides time-consuming, is highly dependent on the machinery configuration as in general the results obtained for a mechanical system cannot be reused by other systems. Moreover, the information about time events is often lost along the process, preventing the discovery of faulty state patterns in machines operating under time-varying conditions. In this paper a novel method for automatic feature extraction and estimation of fault severity is proposed to overcome the drawbacks of classical techniques. The proposed method employs a Deep Convolutional Neural Network pre-trained by a Stacked Convolutional Autoencoder. The robustness and accuracy of this new method are validated using a dataset with different severity conditions on failure mode in a helical gearbox, working in both constant and variable speed of operation. The results show that the proposed unsupervised feature extraction method is effective for the estimation of fault severity in helical gearbox, and it has a consistently better performance in comparison with other reported feature extraction methods. (C) 2017 Elsevier B.V. All rights reserved.R&D projects Ministeriode Economia yCompet-itividad of Cobierno de Espana [TH12012-37434, T1N2013-41086-P]European FEDER fundsGIDTEC [002-002-2016-03-03]Universidad Politecnica Salesians sede CuencaSecretariat for Higher Education, Science.Technology and Innovation (SENESCVT) of the Republic of Ecuadorinfo:eu-repo/semantics/publishedVersio