Cloud Service Provider Evaluation System using Fuzzy Rough Set Technique

Cloud Service Providers (CSPs) offer a wide variety of scalable, flexible, and cost-efficient services to cloud users on demand and pay-per-utilization basis. However, vast diversity in available cloud service providers leads to numerous challenges for users to determine and select the best suitable service. Also, sometimes users need to hire the required services from multiple CSPs which introduce difficulties in managing interfaces, accounts, security, supports, and Service Level Agreements (SLAs). To circumvent such problems having a Cloud Service Broker (CSB) be aware of service offerings and users Quality of Service (QoS) requirements will benefit both the CSPs as well as users. In this work, we proposed a Fuzzy Rough Set based Cloud Service Brokerage Architecture, which is responsible for ranking and selecting services based on users QoS requirements, and finally monitor the service execution. We have used the fuzzy rough set technique for dimension reduction. Used weighted Euclidean distance to rank the CSPs. To prioritize user QoS request, we intended to use user assign weights, also incorporated system assigned weights to give the relative importance to QoS attributes. We compared the proposed ranking technique with an existing method based on the system response time. The case study experiment results show that the proposed approach is scalable, resilience, and produce better results with less searching time.Comment: 12 pages, 7 figures, and 8 table

Ontology-Based Users & Requests Clustering in Customer Service Management System

Customer Service Management is one of major business activities to better serve company customers through the introduction of reliable processes and procedures. Today this kind of activities is implemented through e-services to directly involve customers into business processes. Traditionally Customer Service Management involves application of data mining techniques to discover usage patterns from the company knowledge memory. Hence grouping of customers/requests to clusters is one of major technique to improve the level of company customization. The goal of this paper is to present an efficient for implementation approach for clustering users and their requests. The approach uses ontology as knowledge representation model to improve the semantic interoperability between units of the company and customers. Some fragments of the approach tested in an industrial company are also presented in the paper.Comment: 15 pages, 4 figures, published in Lecture Notes in Computer Scienc

Anomaly Detection for malware identification using Hardware Performance Counters

Computers are widely used today by most people. Internet based applications, like ecommerce or ebanking attracts criminals, who using sophisticated techniques, tries to introduce malware on the victim computer. But not only computer users are in risk, also smartphones or smartwatch users, smart cities, Internet of Things devices, etc. Different techniques has been tested against malware. Currently, pattern matching is the default approach in antivirus software. Also, Machine Learning is successfully being used. Continuing this trend, in this article we propose an anomaly based method using the hardware performance counters (HPC) available in almost any modern computer architecture. Because anomaly detection is an unsupervised process, new malware and APTs can be detected even if they are unknown

Human Attention Estimation for Natural Images: An Automatic Gaze Refinement Approach

Photo collections and its applications today attempt to reflect user interactions in various forms. Moreover, photo collections aim to capture the users' intention with minimum effort through applications capturing user intentions. Human interest regions in an image carry powerful information about the user's behavior and can be used in many photo applications. Research on human visual attention has been conducted in the form of gaze tracking and computational saliency models in the computer vision community, and has shown considerable progress. This paper presents an integration between implicit gaze estimation and computational saliency model to effectively estimate human attention regions in images on the fly. Furthermore, our method estimates human attention via implicit calibration and incremental model updating without any active participation from the user. We also present extensive analysis and possible applications for personal photo collections

What's in a Session: Tracking Individual Behavior on the Web

We examine the properties of all HTTP requests generated by a thousand undergraduates over a span of two months. Preserving user identity in the data set allows us to discover novel properties of Web traffic that directly affect models of hypertext navigation. We find that the popularity of Web sites -- the number of users who contribute to their traffic -- lacks any intrinsic mean and may be unbounded. Further, many aspects of the browsing behavior of individual users can be approximated by log-normal distributions even though their aggregate behavior is scale-free. Finally, we show that users' click streams cannot be cleanly segmented into sessions using timeouts, affecting any attempt to model hypertext navigation using statistics of individual sessions. We propose a strictly logical definition of sessions based on browsing activity as revealed by referrer URLs; a user may have several active sessions in their click stream at any one time. We demonstrate that applying a timeout to these logical sessions affects their statistics to a lesser extent than a purely timeout-based mechanism.Comment: 10 pages, 13 figures, 1 tabl

Web browsing interactions inferred from a flow-level perspective

Desde que su uso se extendiera a mediados de los noventa, la web ha sido probablemente el servicio de Internet más popular. De hecho, muchos usuarios la utilizan prácticamente como sinónimo de Internet. Hoy en día los usuarios de la web utilizan una gran cantidad dispositivos distintos para acceder a ella desde ordenadores tradicionales a teléfonos móviles, tabletas, lectores de libros electrónicos o, incluso, relojes inteligentes. Además, los usuarios se han acostumbrado a acceder a diferentes servicios a través de sus navegadores web en vez de utilizar aplicaciones dedicadas a ello. Este es el caso, por ejemplo del correo electrónico, del streaming de vídeo o de suites ofimáticas (como la proporcionada por Google Docs). Como consecuencia de todo esto, hoy en día el tráfico web es muy complejo y el efecto que tiene en las redes es muy importante. La comunidad científica ha reaccionado a esta situación impulsando muchos estudios que caracterizan la web y su tráfico y que proponen maneras de mejorar su funcionamiento. Sin embargo, muchos estudios centrados en el tráfico web han considerado el tráfico de los clientes o los servidores en su totalidad con el objetivo de describirlo estadísticamente. En otros casos, se han introducido en el nivel de aplicación al centrarse en los mensajes HTTP. Pocos trabajos han buscado describir el efecto que las sesiones de un sitio web y las visitas a páginas web tienen en el tráfico de un usuario. No obstante, esas interacciones son las que el usuario experimenta al navegar y, por tanto, son las que mejor representan su comportamiento. El trabajo que se presenta en esta tesis gira alrededor de esas interacciones y se enfoca especialmente en identificarlas en el tráfico de los usuarios. Esta tesis aborda el problema desde una perspectiva a nivel de flujo. En otras palabras, el estudio que se presenta se centra en una caracterización del tráfico web obtenida para cada conexión mediante datos de los niveles de transporte y red, nunca mediante datos de aplicación. La perspectiva a nivel de flujo introduce ciertas limitaciones en las propuestas desarrolladas, pero lo compensa al permitir desarrollar sistemas escalables, fáciles de instalar en cualquier red y que evitan acceder a información de usuario que podría ser sensible. En los capítulos de este documento se introducen varios métodos para identificar sesiones a sitios web y descargas de páginas web en el tráfico de los usuarios. Para desarrollar dichos métodos se ha caracterizado tráfico web capturado de varias formas: accediendo a páginas automáticamente, con la ayuda de voluntarios en un entorno controlado y en el enlace de la Universidad Pública de Navarra. Los métodos que presentamos se basan en parámetros a nivel de conexión como los tiempos de inicio y final de los flujos o las direcciones IP de servidor. Estos parámetros se emplean para encontrar conexiones relacionadas en el tráfico de los usuarios. La validación de los resultados obtenidos con los distintos métodos ha sido complicada al no disponer de trazas etiquetadas correctamente que puedan usarse para verificar que las clasificaciones se han realizado de forma correcta. Además, al no haber propuestas similares en la literatura científica ha sido imposible comparar los resultados obtenidos con los de otros autores. Por todo esto ha sido necesario diseña métodos específicos de validación que también se describen en este documento. Ser capaces de identificar sesiones a sitios web y descargas de páginas web tiene aplicaciones inmediatas para administradores de red y proveedores de servicio ya que les permitiría recoger datos sobre el perfil de navegación de sus usuarios e incluso bloquear tráfico indeseado y dar prioridad al importante. Además, las ventajas de trabajar a nivel de conexión se aplican especialmente en su caso. Por último, los resultados obtenidos a través de los métodos presentados en esta tesis podrían emplearse en diseñar esquemas capaces de clasificar el tráfico web dependiendo del servicio que lo haya producido ya que se podrían utilizar como parámetros de entrada las características de múltiples conexiones relacionadas.Since its use became widespread during the mid 1990s, the web has probably been the most popular Internet service. In fact, for many lay users, the web is almost a synonym for the Internet. Web users today access it from a myriad of different devices from traditional computers to smartphones, tablets, ebook readers and even smart watches. Moreover, users have become accustomed to accessing multiple different services through their web browsers instead of through dedicated applications. This is the case, for example, of e-mail, video-streaming or office suites (such as the one provided by Google Docs). As a consequence, web traffic nowadays is complex and its effect on the networks is very important. The scientific community has reacted to this providing many works that characterize the web and its traffic and propose ways of improving its operation. Nevertheless, studies focused on web traffic have often considered the traffic of web clients or servers as a whole in order to describe their particular performance, or have delved into the application level by focusing on HTTP messages. Few works have attempted to describe the effect of website sessions and webpage visits on web traffic. Those web browsing interactions are, however, the elements of web operation that the user actually experiences and thus are the most representative of his behavior. The work presented in this thesis revolves around these web interactions with the special focus of identifying them in user traffic. This thesis offers a distinctive approach in that the problem at hand is faced from a flow-level perspective. That is, the study presented here centers on a characterization of web traffic obtained on a per connection basis and using information from the transport and network levels rather than relying on deep packet inspection. This flow-level perspective introduces various constraints to the proposals developed, but pays off by offering scalability, ease of deployment, and by avoiding the need to access potentially sensitive application data. In the chapters of this document, different methods for identifying website sessions and webpage downloads in user traffic are introduced. In order to develop those methods, web traffic is characterized from a connection perspective using traces captured by accessing the web automatically, with the help of voluntary users in a controlled environment, and captured in the wild from users of the Public University of Navarre. The methods rely on connection-level parameters such as start and end timestamps or server IP addresses in order to find related connections in the traffic of web users. Evaluating the performance of the different methods has been problematic because of the absence of ground truth (labeled web traffic traces are hard to obtain and the labeling process is very complex) and the lack of similar research which could be used for comparison purposes. As a consequence, specific validation methods have been designed and they are also described in this document. Identifying website sessions and webpage downloads in user traffic has multiple immediate applications for network administrators and Internet service providers as it would allow them to gather additional insight into their users browsing behavior and even block undesired traffic or prioritize important one. Moreover, the advantages of a connection-level perspective would be specially interesting for them. Finally, this work could also help in research directed to classifying thee services provided through the web as grouping the connections related to the same website session may offer additional information for the classification process.Programa Oficial de Doctorado en Tecnologías de las Comunicaciones (RD 1393/2007)Komunikazioen Teknologietako Doktoretza Programa Ofiziala (ED 1393/2007

IMPRECISE EMPIRICAL ONTOLOGY REFINEMENT Application to Taxonomy Acquisition

ontology engineering; ontology learning; taxonomy acquisiton; uncertainty The significance of uncertainty representation has become obvious in the Semantic Web community recently. This paper presents new results of our research on uncertainty incorporation into ontologies created automatically by means of Human Language Technologies. The research is related to OLE (Ontology LEarning) a – a project aimed at bottom-up generation and merging of ontologies. It utilises a proposal of expressive fuzzy knowledge representation framework called ANUIC (Adaptive Net of Universally Interrelated Concepts). We discuss our recent achievements in taxonomy acquisition and show how even simple application of the principles of ANUIC can improve the results of initial knowledge extraction methods. a The project’s web page can be found a