Ricerc@Sapienza

With the breakthrough of pervasive advanced networking infrastructures and paradigms such as 5G and IoT, cybersecurity became an active and crucial field in the last years. Furthermore, machine learning techniques are gaining more and more attention as prospective tools for mining of (possibly malicious) packet traces and automatic synthesis of network intrusion detection systems. In this work, we propose a modular ensemble of classifiers for spotting malicious attacks on Wi-Fi networks.

Microgrids (MGs) play a crucial role for the development of Smart Grids. They are conceived to intelligently integrate the generation from Distributed Energy Resources, to improve Demand Response (DR) services, to reduce pollutant emissions and curtail power losses, assuring the continuity of services to the loads as well. In this work it is proposed a novel synthesis procedure for modelling an Adaptive Neuro-Fuzzy Inference System (ANFIS) featured by multivariate Gaussian Membership Functions (MFs) and ?rst order Takagi-Sugeno rules.

In this paper we discuss techniques for potential speedups in k-medoids clustering. Specifically, we address the advantages of pre-caching the pairwise distance matrix, heart of the k-medoids clustering algorithm, not only in order to speedup the execution of the algorithm itself, but also in order to speedup the evaluation of the well-known Silhouette Index and Davies-Bouldin Index for clusters’ validation. A major disadvantage of such pre-caching is that it might not be suitable for large datasets.