SS-IDS: Statistical Signature based IDS

Security of web servers has become a sensitive subject today. Prediction of normal and abnormal request is problematic due to large number of false alarms in many anomaly based Intrusion Detection Systems(IDS). SS-IDS derives automatically the parameter profiles from the analyzed data thereby generating the Statistical Signatures. Statistical Signatures are based on modeling of normal requests and their distribution value without explicit intervention. Several attributes are used to calculate the behavior of the legitimate request on the web server. SS-IDS is best suited for the newly installed web servers which doesn’t have large number of requests in the data set to train the IDS and can be used on top of currently used signature based IDS like SNORT. Experiments conducted on real data sets have shown high accuracy up to 99.98 % for predicting valid request as valid and false positive rate ranges from 3.82-7.84%. 1

Ruptura de las barreras de los LMS. El mLearning

La ponencia “Ruptura de las barreras de los LMS. El mLearning” fue impartida el 27 de marzo de 2012 en la Academia de Logística del Ejército de Tierra en Calatayud en el contexto del IX Curso básico de enseñanza en entornos virtuales de aprendizaje, organizado por la Subdirección de Enseñanza a Distancia de la Academia de Logística de Calatayud

Modelado de servicios en contextos web. Aplicación en ecosistemas de aprendizaje

[ES]Principios de arquitecturas orientadas a servicios en contextos de aplicaciones web aplicadas a ecosistemas de aprendizaje. Conferencia invitada en los másteres de Sistemas Inteligentes e Ingeniería Informática de la Universidad de Salamanca, impartida el 14 de marzo de 2016 en la Facultad de Ciencias de la USAL

Ruptura y apertura de los entornos LMS: eLearning 2.0

La ponencia “Ruptura y apertura de los entornos LMS: eLearning 2.0” fue impartida el 30 de junio de 2011 en el Curso de Verano de la Universidad de Salamanca “ Aprender con tecnología: La pizarra digital interactiva SMART Board”. La conferencia tenía como objetivo comenzar a presentar la idea de que los LMS empiezan a perder su hegemonía como soporte tecnológico por excelencia a los procesos de enseñanza/aprendizaje. Se abordan tópicos relacionados con el conocimiento digital, el eLearning 2.0 y las redes sociales. Esta presentación se ha actualizado para generar este recurso en marzo de 2015

Presentation of the GRIAL research group and its main research lines and projects on March 2016

[EN]Presentation of the GRIAL research group and its main research lines and projects in the Intelligent System Master Degree of University of Salamanca on March 7th, 2016

A service concept recommendation system for enhancing the dependability of semantic service matchmakers in the service ecosystem environment

A Service Ecosystem is a biological view of the business and software environment, which is comprised of a Service Use Ecosystem and a Service Supply Ecosystem. Service matchmakers play an important role in ensuring the connectivity between the two ecosystems. Current matchmakers attempt to employ ontologies to disambiguate service consumers’ service queries by semantically classifying service entities and providing a series of human computer interactions to service consumers. However, the lack of relevant service domain knowledge and the wrong service queries could prevent the semantic service matchmakers from seeking the service concepts that can be used to correctly represent service requests. To resolve this issue, in this paper, we propose the framework of a service concept recommendation system, which is built upon a semantic similarity model.This system can be employed to seek the concepts used to correctly represent service consumers’ requests, when a semantic service matchmaker finds that the service concepts that are eventually retrieved cannot match the service requests. Whilst many similar semantic similarity models have been developed to date, most of them focus on distance-based measures for the semantic network environment and ignore content-based measures for the ontology environment. For the ontology environment in which concepts are defined with sufficient datatype properties, object properties, and restrictions etc., the content of concepts should be regarded as an important factor in concept similarity measures. Hence, we present a novel semantic similarity model for the service ontology environment. The technical details and evaluation details of the framework are discussed in this paper