No abstract

As the number of cyber‐attacks on financial institutions has increased over the past few years, an advanced system that is capable of predicting the target of an attack is essential. Such a system needs to be integrated into the existing detection systems of financial institutions as it provides them with proactive controls with which to halt an attack by predicting patterns. Advanced prediction systems also enhance the software design and security testing of new advanced cyber‐security measures by providing new testing scenarios supported by attack forecasting. This present study developed a model that forecasts future network‐based cyber‐attacks on financial institutions using a deep neural network. The dataset that was used to train and test the model consisted of some of the biggest cyber‐attacks on banking institutions over the past three years. This provided insight into new patterns that may end with a cyber‐crime. These new attacks were also evaluated to determine behavioral similarities with the nearest known attack or a combination of several existing attacks. The performance of the forecasting model was then evaluated in a real banking environment and provided a forecasting accuracy of 90.36%. As such, financial institutions can use the proposed forecasting model to improve their security testing measures.

Acquiring the skills needed to solve certain types of problems is one of the main challenges of distance university education, demanding extra motivation from students to tackle it. New technology should be one of our great allies in addressing these problems. This article proposes an expert system with a web‐based problem‐solving simulator for a multidevice environment in order to allow students to access an unlimited number of problems with their corresponding solutions, immediately, anytime and anywhere. The types of problems that can be used are those based on quantitative methods with a fixed methodology to be followed. A successful case study was carried out for the subject Operations Management at the Distance University of Madrid (UDIMA), with three different problem simulators having been developed. The results were very satisfactory compared with previous academic years in terms of motivation. A decrease in students that did not sit the final examination was observed, as was an increase in the number of visits to the Moodle classroom. Students also responded positively in their assessments of the initiative.

Los sistemas meteorológicos, como es el Sistema Mundial de Información Global de la Organización Meteorológica Mundial, necesitan almacenar diferentes tipos de imágenes, datos y archivos. Big Data y su modelo 3V puede proporcionar una solución adecuada para resolver este problema. Este tutorial presenta algunos conceptos en torno al framework Hadoop, la implementación y estándar de facto de Big Data, y la forma de almacenar los datos semiestructurados generados por las estaciones meteorológicas automáticas usando este framework. Finalmente, se presenta un método formal para generar informes del tiempo utilizando los frameworks que conforman el ecosistema de Hadoop.

No abstract