No abstract

Critical Infrastructures in public administration would be compromised by Advanced Persistent Threats (APT) which today constitute one of the most sophisticated ways of stealing information. This paper presents an effective, learning based tool that uses inductive techniques to analyze the information provided by firewall log files in an IT infrastructure, and detect suspicious activity in order to mark it as a potential APT. The experiments have been accomplished mixing real and synthetic data traffic to represent different proportions of normal and anomalous activity.

Cybersecurity aggregates are numerical data obtained by aggregation on features along a database of cybersecurity reports. These aggregates are obtained by integration of time-stamped tables using some recent results of non-standard calculus. Time-series of aggregates are shown to contain relevant information about the concrete system dealt with. Trend time series is also forecasted using known data-driven methods. Although absolute forecasting of trend time series is not obtained, a directional forecasting of trend time series is achieved thence validated by means of a rolling cross validation scheme on a public database of Scareware reports.

Large telescopes have important challenges in the near future. Increasing the size of mirrors and sensors suppose not only a design issue, but also new computational techniques are needed to deal with the large amount of data. Adaptive Optics is an essential part of extremely large telescopes, and it uses reference stars and a tomographic reconstructor to compensate the aberrations introduced by the atmosphere during observation. The Complex Atmospheric Reconstructor based on Machine lEarNing (CARMEN) is a tomographic reconstructor based on neural networks which has been used during on-sky observations. In this paper CARMEN will be implemented in two different neural network frameworks, which use a Graphics Processing Unit to improve their performance. To time the training and execution will provide results of which framework is faster for its implementation in a real telescope and will supply new tools to keep improving the reconstruction ability of CARMEN.

The approach of the education in the university and not university contexts in which we are nowadays makes it necessary to change the methodologies and dynamics developed by teachers in the classroom. This work arises from the necessity of applying active methodologies to the training of university student, and also to the search for functionality and motivation in learning, having the use of instruments and tools with which the student becomes aware of his own teaching-learning process and fosters the critical spirit. The motivation to undertake this practice is raised by the concern to provide the student with skills to help them in their professional development. In the current article it is tried to verify to what extent these formative assessment dynamics influence the level of acquisition of the competences, both specific and transversal, of a subject of the Degree in Electrical Engineering.