Cybersecurity Alert Prioritization in a Critical High Power Grid With Latent Spaces

High-Power electric grid networks require extreme security in their associated telecommunication network to ensure protection and control throughout power transmission. Accordingly, supervisory control and data acquisition systems form a vital part of any critical infrastructure, and the safety of the associated telecommunication network from intrusion is crucial. Whereas events related to operation and maintenance are often available and carefully documented, only some tools have been proposed to discriminate the information dealing with the heterogeneous data from intrusion detection systems and to support the network engineers. In this work, we present the use of deep learning techniques, such as Autoencoders or conventional Multiple Correspondence Analysis, to analyze and prune the events on power communication networks in terms of categorical data types often used in anomaly and intrusion detection (such as addresses or anomaly description). This analysis allows us to quantify and statistically describe highseverity events. Overall, portions of alerts around 5-10% have been prioritized in the analysis as first to handle by managers. Moreover, probability clouds of alerts have been shown to configure explicit manifolds in latent spaces. These results offer a homogeneous framework for implementing anomaly detection prioritization in power communication networks

Aprendizaje basado en la interacción de usuarios para búsqueda y recuperación de imágenes

Se propone, al amparo del proyecto “Nuevos Algoritmos para la Gestión Eficiente de Contenidos Multimedia/nen Redes de Comunicaciones Móviles” (NAGEC), un nuevo mecanismo para la búsqueda y recuperación de/nimágenes basado en realimentación de relevancia. La arquitectura propuesta se compone de una red neuronal y/nun tesauro. La red neuronal extrae de las imágenes dos parámetros: textura y color. El tesauro recoge las/nrelaciones semánticas existentes entre los términos descriptores de las imágenes de la base de datos VisTex./nAmbos componentes se relacionan mediante un modelo de realimentación de relevancia que, a través de las/ninteracciones del usuario con el tesauro durante el proceso de búsqueda, permite a la red aprender relaciones/nsemánticas inherentes a las imágenes.Este artículo ha sido parcialmente financiado por el/nproyecto CICYT TIC 2002-03713

Aprendizaje basado en la interacción de usuarios para búsqueda y recuperación de imágenes

Se propone, al amparo del proyecto “Nuevos Algoritmos para la Gestión Eficiente de Contenidos Multimedia/nen Redes de Comunicaciones Móviles” (NAGEC), un nuevo mecanismo para la búsqueda y recuperación de/nimágenes basado en realimentación de relevancia. La arquitectura propuesta se compone de una red neuronal y/nun tesauro. La red neuronal extrae de las imágenes dos parámetros: textura y color. El tesauro recoge las/nrelaciones semánticas existentes entre los términos descriptores de las imágenes de la base de datos VisTex./nAmbos componentes se relacionan mediante un modelo de realimentación de relevancia que, a través de las/ninteracciones del usuario con el tesauro durante el proceso de búsqueda, permite a la red aprender relaciones/nsemánticas inherentes a las imágenes.Este artículo ha sido parcialmente financiado por el/nproyecto CICYT TIC 2002-03713

Spectral Analysis and Mutual Information Estimation of Left and Right Intracardiac Electrograms during Ventricular Fibrillation

Ventricular fibrillation (VF) signals are characterized by highly volatile and erratic electrical impulses, the analysis of which is difficult given the complex behavior of the heart rhythms in the left (LV) and right ventricles (RV), as sometimes shown in intracardiac recorded Electrograms (EGM). However, there are few studies that analyze VF in humans according to the simultaneous behavior of heart signals in the two ventricles. The objective of this work was to perform a spectral and a non-linear analysis of the recordings of 22 patients with Congestive Heart Failure (CHF) and clinical indication for a cardiac resynchronization device, simultaneously obtained in LV and RV during induced VF in patients with a Biventricular Implantable Cardioverter Defibrillator (BICD) Contak Renewal IVTM (Boston Sci.). The Fourier Transform was used to identify the spectral content of the first six seconds of signals recorded in the RV and LV simultaneously. In addition, measurements that were based on Information Theory were scrutinized, including Entropy and Mutual Information. The results showed that in most patients the spectral envelopes of the EGM sources of RV and LV were complex, different, and with several frequency peaks. In addition, the Dominant Frequency (DF) in the LV was higher than in the RV, while the Organization Index (OI) had the opposite trend. The entropy measurements were more regular in the RV than in the LV, thus supporting the spectral findings. We can conclude that basic stochastic processing techniques should be scrutinized with caution and from basic to elaborated techniques, but they can provide us with useful information on the biosignals from both ventricles during VF

Data Science Analysis and Profile Representation Applied to Secondary Prevention of Acute Coronary Syndrome

The analysis of large amounts of data from electronic medical records (EMRs) and daily clinical practice data sources has received increasing attention in the last years. However, few systematic approaches have been proposed to support the extraction of the wealth and diversity of information from these data sources. Specifically, Acute Coronary Syndrome (ACS) data are available in many hospitals and health units because ACS shows elevated morbidity and mortality. This work proposes a method called Data Science Analysis and Representation (DSAR) to scrutinize and exploit, in a univariate way, scientific information content in limited ACS samples. DSAR uses Bootstrap Resampling to provide robust, cross-sectional, and non-parametric statistical tests on categorical and metric variables. It also constructs an informative graphical representation of the database variables, which helps to interpret the results and to identify the relevant variables. Our objectives were to validate DSAR by comparing it to conventional statistical methods when looking for the most relevant variables in the secondary prevention of ACS, and to determine the degree of correlation between them and the Exitus event (associated with patient death). To achieve this objective, we applied DSAR on an anonymized sample of 270 variables from 2377 patients diagnosed with ACS. The results showed that DSAR identified 44% significant variables while conventional methods offered weak correlation results. Then, the scientific literature was reviewed for a set of these variables, validating the agreement with clinical experience and previous ACS research. The conclusion is that DSAR is a valuable and a useful method for clinicians in the identification of potentially predictive variables and, overall, a good starting point for future multivariate secondary analyzes in the clinical field of ACS, or fields with similar information characteristics