This study introduces and describes a novel intrusion detection system (IDS) called MOVCIDS (mobile visualization connectionist IDS). This system applies neural projection architectures to detect anomalous situations taking place in a computer network. By its advanced visualization facilities, the proposed IDS allows providing an overview of the network traffic as well as identifying anomalous situations tackled by computer networks, responding to the challenges presented by volume, dynamics and diversity of the traffic, including novel (0-day) attacks. MOVCIDS provides a novel point of view in the field of IDSs by enabling the most interesting projections (based on the fourth order statistics; the kurtosis index) of a massive traffic dataset to be extracted. These projections are then depicted through a functional and mobile visualization interface, providing visual information of the internal structure of the traffic data. The interface makes MOVCIDS accessible from any mobile device to give more accessibility to network administrators, enabling continuous visualization, monitoring and supervision of computer networks. Additionally, a novel testing technique has been developed to evaluate MOVCIDS and other IDSs employing numerical datasets. To show the performance and validate the proposed IDS, it has been tested in different real domains containing several attacks and anomalous situations. In addition, the importance of the temporal dimension on intrusion detection, and the ability of this IDS to process it, are emphasized in this workJunta de Castilla and Leon project BU006A08, Business intelligence for production within the framework of the Instituto Tecnologico de Cas-tilla y Leon (ITCL) and the Agencia de Desarrollo Empresarial (ADE), and the Spanish Ministry of Education and Innovation project CIT-020000-2008-2. The authors would also like to thank the vehicle interior manufacturer, Grupo Antolin Ingenieria S. A., within the framework of the project MAGNO2008-1028-CENIT Project funded by the Spanish Government

Human Activity Recognition (HAR) is aimed at identifying current subject task performed by a person as a result of analyzing data from wearable sensors. HAR is a very challenging task that has been applied in different areas such as rehabilitation and localization. During the past ten years, plenty of models, number of sensors and sensor placements, and feature transformations have been reported for this task. From this bunch of previous ideas, what seems to be clear is that the very specific applications drive to the selection of the best choices for each case.Present research is focused on early diagnosis of stroke, what involves reducing the feature space of gathered data and subsequent HAR, among other tasks. In this study, an Information Correlation Coefficient (ICC) analysis was carried out followed by a wrapper Feature Selection (FS) method on the reduced input space. Additionally, a novel HAR method is proposed for this specific problem of stroke early diagnosing, comprising an adaptation of the well-known Genetic Fuzzy Finite State Machine (GFFSM) method.To the best of the author¿s knowledge, this is the very first analysis of the feature space concerning all the previously published feature transformations on raw acceleration data. The main contributions of this study are the optimization of the sample rate, selection of the best feature subset, and learning of a suitable HAR method based on GFFSM to be applied to the HAR problem.

This study presents a multiagent architecture aimed at detecting SQL injection attacks, which are one of the most prevalent threats for modern databases. The proposed architecture is based on a hierarchical and distributed strategy where the functionalities are structured on layers. SQL-injection attacks, one of the most dangerous attacks to online databases, are the focus of this research. The agents in each one of the layers are specialized in specific tasks, such as data gathering, data classification, and visualization. This study presents two key agents under a hybrid architecture: a classifier agent that incorporates a Case-Based Reasoning engine employing advanced algorithms in the reasoning cycle stages, and a visualizer agent that integrates several techniques to facilitate the visual analysis of suspicious queries. The former incorporates a new classification model based on a mixture of a neural network and a Support Vector Machine in order to classify SQL queries in a reliable way. The latter combines clustering and neural projection techniques to support the visual analysis and identification of target attacks. The proposed approach was tested in a real-traffic case study and its experimental results, which validate the performance of the proposed approach, are presented in this paperSpanish Ministry of Science projects OVAMAH (TIN 2009-13839-C03-03) and MIDAS (TIN 2010-21272-C02-01), funded by the European Regional Development Fund, projects of the Junta of Castilla and Leon BU006A08 and JCYL-2002-05; Projects of the Spanish Government SA071A08, CIT-020000-2008-2 and CIT-020000-2009-12; the Professional Excellence Program 2006-2010 IFARHU-SENACYT-Panama. The authors would also like to thank the vehicle interior manufacturer, Grupo Antolin Ingenieria S.A., within the framework of the project MAGNO2008 - 1028. - CENIT Project funded by the Spanish Ministry