Metodología para el análisis y desarrollo de un sistema de información basado en imágen : un caso práctico de implementación en un servicio de hemodinámica

[Resumen] Las patologías asociadas al corazón constituyen uno de los mayores problemas de salud en el mundo occidental, De entre ellas, la oclusión coronaria es una de las enfermedades de mayor relevancia, debido a su índice de mortalidad y morbilidad. Ante síntomas evidentes de problemas cardiovasculares, la técnica de diagnóstico más utilizada es la angiografía. Dicho estudio permite al clínico observar el flujo sanguíneo en las arterias coronarias, detectando los estrechamientos acusados o "estenosis". En función de la severidad, extensión y ubicación de las estenosis, el clínico realiza el diagnóstico del paciente, define un tratamiento y establece el pronóstico de la enfermedad. Actualmente, los clínicos observan las secuencias de imágenes y, en función de su conocimiento empírico, toman las decisiones oportunas. La implantación de la radiología digital, la información asociada a los pacientes, el creciente número de estudios de imagen que se realizan y la necesidad de disponer de un acceso rápido y eficaz a esta información de forma ubicua ha puesto de manifiesto la importancia de los sistemas de información en el ámbito clínico, como pueden ser los Sistemas de Archivo y Comunicación de Imágenes Médicas. En este trabajo, se presenta un sistema de información de apoyo a la toma de decisión clínica de cardiopatías basado en estudios de angiografía

Use of Machine Learning Algorithms for Network Traffic Classification

Cursos e Congresos , C-155[Abstract] In recent years, the complexity of threats utilizing the network as an attack vector has significantly increased. Traditional attack prevention and detection systems (IPS/IDS) based on signatures do not provide an acceptable level of security for many organizations. Furthermore, the volume of traffic on corporate networks has also grown exponentially, while quality of service requirements do not always allowfor deep inspection (at the application layer) of packets. The main objective of this work is to demonstrate that the application of machine learning techniques to the information of data flows circulating through the network allows for the satisfactory detection of malicious traffic. Specifically, this work is developed within an emerging network paradigm, such as software-defined networksThis research has been funded by the Spanish Ministry of Economy and Competitiveness and European Union ERDF funds (Project PID2019-111388GB-I00). CITIC is funded by the Xunta de Galicia through the collaboration agreement between the Conseller´ıa de Cultura, Educación, Formación Profesional e Universidades and the Galician universities for the reinforcement of the research centres of the Galician University System (CIGUS

Un concurso de cortos para el refuerzo pedagógico y la mejora de la participación del alumnado

[Resumen] En la asignatura de Redes del Grado en Ingeniería Informática de la Universidade da Coruña se explican los fundamentos de la comunicación a través de una red de computadores. Para incentivar la participación del alumnado e incrementar su motivación se ha propuesto un concurso de cortometrajes. Se busca que el alumno sea un protagonista activo del aprendizaje, en clara sintonía con el propósito de la reforma educativa actual. El objetivo de la actividad es que el alumno cree un vídeo de un máximo de 3 minutos de duración en el que explique un concepto. Posteriormente, se realiza una evaluación en base a una rúbrica. Varios alumnos y profesores juzgan cada vídeo de tal manera que los evaluados no conocen a sus evaluadores. El beneficio de la actividad es doble: los estudiantes que preparan los vídeos deben estudiar el material, y los estudiantes que ven los vídeos aprenden de un modo más informal y divertido. Para conseguir más retroalimentación, se han proporcionado encuestas a los alumnos, y los resultados han sido muy positivos. Además, se han conseguido vídeos de buena calidad, que se pueden utilizar como material docente.[Abstract] In the subject of Networks of the Degree in Computer Engineering of the University of A Coruna, the fundamentals of communication through a network of computers are explained. A short film contest has been proposed in order to foster the participation and increase the motivation of the students. It is intended that the student is the protagonist, in clear harmony with the purpose of the current educational reform. The aim of this activity is to explain a concept in a video 3 minutes long as maximum. Then, the videos are evaluated by several students and teachers using a rubric according to a blind evaluation. The benefits of this activity are two: students who prepare the videos must understand the concepts, and students who watch the videos learn in a more informal and funny way. A survey has been provided to the students and the results have been very positive. Moreover, good-quality videos have been obtained, so they can be employed as teaching material

IoT Dataset Validation Using Machine Learning Techniques for Traffic Anomaly Detection

This article belongs to the Special Issue Sensor Network Technologies and Applications with Wireless Sensor Devices[Abstract] With advancements in engineering and science, the application of smart systems is increasing, generating a faster growth of the IoT network traffic. The limitations due to IoT restricted power and computing devices also raise concerns about security vulnerabilities. Machine learning-based techniques have recently gained credibility in a successful application for the detection of network anomalies, including IoT networks. However, machine learning techniques cannot work without representative data. Given the scarcity of IoT datasets, the DAD emerged as an instrument for knowing the behavior of dedicated IoT-MQTT networks. This paper aims to validate the DAD dataset by applying Logistic Regression, Naive Bayes, Random Forest, AdaBoost, and Support Vector Machine to detect traffic anomalies in IoT. To obtain the best results, techniques for handling unbalanced data, feature selection, and grid search for hyperparameter optimization have been used. The experimental results show that the proposed dataset can achieve a high detection rate in all the experiments, providing the best mean accuracy of 0.99 for the tree-based models, with a low false-positive rate, ensuring effective anomaly detection.This project was funded by the Accreditation, Structuring, and Improvement of Consolidated Research Units and Singular Centers (ED431G/01), funded by Vocational Training of the Xunta de Galicia endowed with EU FEDER funds and Spanish Ministry of Science and Innovation, via the project PID2019-111388GB-I00Xunta de Galicia; ED431G/0

Network Anomaly Detection Using Machine Learning Techniques

[Abstract] While traditional network security methods have been proven useful until now, the flexibility of machine learning techniques makes them a solid candidate in the current scene of our networks. In this paper, we assess how well the latter are capable of detecting security threats in a corporative network. To that end, we configure and compare several models to find the one which fits better with our needs. Furthermore, we distribute the computational load and storage so we can handle extensive volumes of data. The algorithms that we use to create our models, Random Forest, Naive Bayes, and Deep Neural Networks (DNN), are both divergent and tested in other papers in order to make our comparison richer. For the distribution phase, we operate with Apache Structured Streaming, PySpark, and MLlib. As for the results, it is relevant to mention that our dataset has been found to be effectively modelable with just a reduced number of features. Finally, given the outcomes obtained, we find this line of research encouraging and, therefore, this approach worth pursuing

Early Detection of Depression: Social Network Analysis and Random Forest Techniques

[Abstract] Background: Major depressive disorder (MDD) or depression is among the most prevalent psychiatric disorders, affecting more than 300 million people globally. Early detection is critical for rapid intervention, which can potentially reduce the escalation of the disorder. Objective: This study used data from social media networks to explore various methods of early detection of MDDs based on machine learning. We performed a thorough analysis of the dataset to characterize the subjects’ behavior based on different aspects of their writings: textual spreading, time gap, and time span. Methods: We proposed 2 different approaches based on machine learning singleton and dual. The former uses 1 random forest (RF) classifier with 2 threshold functions, whereas the latter uses 2 independent RF classifiers, one to detect depressed subjects and another to identify nondepressed individuals. In both cases, features are defined from textual, semantic, and writing similarities. Results: The evaluation follows a time-aware approach that rewards early detections and penalizes late detections. The results show how a dual model performs significantly better than the singleton model and is able to improve current state-of-the-art detection models by more than 10%. Conclusions: Given the results, we consider that this study can help in the development of new solutions to deal with the early detection of depression on social networks.Ministerio de Economía y Competitividad; TIN2015-70648-PXunta de Galicia; ED431G/01 2016-201

Time-Aware Detection Systems

[Abstract] Communication network data has been growing in the last decades and with the generalisation of the Internet of Things (IoT) its growth has increased. The number of attacks to this kind of infrastructures have also increased due to the relevance they are gaining. As a result, it is vital to guarantee an adequate level of security and to detect threats as soon as possible. Classical methods emphasise in detection but not taking into account the number of records needed to successfully identify an attack. To achieve this, time-aware techniques both for detection and measure may be used. In this work, well-known machine learning methods will be explored to detect attacks based on public datasets. In order to obtain the performance, classic metrics will be used but also the number of elements processed will be taken into account in order to determine a time-aware performance of the method.Ministero de Economía y Competitividad; TIN2015-70648-PXunta de Galicia; ED431G/01 2016-201

Early Detection of Cyberbullying on Social Media Networks

[Abstract] Cyberbullying is an important issue for our society and has a major negative effect on the victims, that can be highly damaging due to the frequency and high propagation provided by Information Technologies. Therefore, the early detection of cyberbullying in social networks becomes crucial to mitigate the impact on the victims. In this article, we aim to explore different approaches that take into account the time in the detection of cyberbullying in social networks. We follow a supervised learning method with two different specific early detection models, named threshold and dual. The former follows a more simple approach, while the latter requires two machine learning models. To the best of our knowledge, this is the first attempt to investigate the early detection of cyberbullying. We propose two groups of features and two early detection methods, specifically designed for this problem. We conduct an extensive evaluation using a real world dataset, following a time-aware evaluation that penalizes late detections. Our results show how we can improve baseline detection models up to 42%.This research was supported by the Ministry of Economy and Competitiveness of Spain and FEDER funds of the European Union (Project PID2019-111388GB-I00) and by the Centro de Investigación de Galicia “CITIC”, funded by Xunta de Galicia (Galicia, Spain) and the European Union (European Regional Development Fund — Galicia 2014–2020 Program) , by grant ED431G 2019/01Xunta de Galicia; ED431G 2019/0

A genetic algorithms-based approach for optimizing similarity aggregation in ontology matching

[Abstract] Ontology matching consists of finding the semantic relations between different ontologies and is widely recognized as an essential process to achieve an adequate interoperability between people, systems or organizations that use different, overlapping ontologies to represent the same knowledge. There are several techniques to measure the semantic similarity of elements from separate ontologies, which must be adequately combined in order to obtain precise and complete results. Nevertheless, combining multiple similarity measures into a single metric is a complex problem, which has been traditionally solved using weights determined manually by an expert, or through general methods that do not provide optimal results. In this paper, a genetic algorithms based approach to aggregate different similarity metrics into a single function is presented. Starting from an initial population of individuals, each one representing a combination of similarity measures, our approach allows to find the combination that provides the optimal matching quality.Instituto de Salud Carlos III; FISPI10/02180Programa Iberoamericano de Ciencia y Tecnología para el Desarrollo; 209RT0366Xunta de Galicia; CN2012/217Xunta de Galicia; CN2011/034Xunta de Galicia; CN2012/21

AI-based user authentication reinforcement by continuous extraction of behavioral interaction features

Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.[Abstract]: In this work, we conduct an experiment to analyze the feasibility of a continuous authentication method based on the monitorization of the users' activity to verify their identities through specific user profiles modeled via Artificial Intelligence techniques. In order to conduct the experiment, a custom application was developed to gather user records in a guided scenario where some predefined actions must be completed. This dataset has been anonymized and will be available to the community. Additionally, a public dataset was also used for benchmarking purposes so that our techniques could be validated in a non-guided scenario. Such data were processed to extract a number of key features that could be used to train three different Artificial Intelligence techniques: Support Vector Machines, Multi-Layer Perceptrons, and a Deep Learning approach. These techniques demonstrated to perform well in both scenarios, being able to authenticate users in an effective manner. Finally, a rejection test was conducted, and a continuous authentication system was proposed and tested using weighted sliding windows, so that an impostor could be detected in a real environment when a legitimate user session is hijacked.Xunta de Galicia; ED431G 2019/01Xunta de Galicia; ED431B 2021/36Xunta de Galicia; ED481A-2019/155This work made use of the infrastructures acquired with Grants provided by the State Research Agency (AEI) of the Spanish Government and the European Regional Development Fund (FEDER), through RTI2018-095076-B-C22, and PID2019-525 111388GB-I00. We acknowledge support from CIGUS-CITIC, funded by Xunta de Galicia and the European Union (FEDER Galicia 2014-2020 Program) through Grant ED431G 2019/01; research consolidation Grant ED431B 2021/36; Art.83 collaboration F19/03 with the enterprise Odeene S.L.; and scholarship from Xunta de Galicia and the European Union (European Social Fund - ESF) ED481A-2019/155