In the last two decades, one of the most important topics of discussion is the environmental problem, and to solve the latter, scientists have proposed several types of solutions. Recently Smart Grid concept has taken hold. The latter was presented in 2007 to manage electricity and integrate renewable energy sources. In that context, one of the problems is obtaining power system stability. This pertains to the ability of an electrical grid, transforming smart grid technologies, to maintain steadfast and dependable operation despite the inherent variability introduced by renewable resources. In this paper, we propose a technique to determine if a network is stable or unstable by employing several deep-learning architectures. In detail, we used a dataset retrieved from Kaggle, where all data have been converted into images thanks to the usage of a Python script designed and built by the authors. After getting the related pictures, the new dataset obtained was divided into 80-10-10 (training-validation-test). After this step, we executed several experiments where we reached an accuracy and precision of 0.998. In particular, to perform them we employed different Convolutional Neural Networks present in the state-of-the-art like AlexNet, DenseNet, LeNet, ResNet50, Standard CNN, VGG16, and VGG19. In the final step, to perform the explainability, we also employed three different CAMs Grad-CAM, Grad-CAM++, and Score-CAM, to highlight the most distinctive areas of an image.

Artificial intelligence has increasingly pervaded both everyday life and business environments, assuming a role in supporting human decision-making. These systems have grown progressively intricate and effective, carrying the potential to unearth valuable insights across various applications. Nevertheless, for Artificial Intelligence to gain widespread acceptance, human trust in its outcomes is essential. Trustworthy Artificial Intelligence is a term used to describe legal, ethically compliant, and technically sound Artificial Intelligence. In this paper, we design a proposal with the aim to introduce trustworthiness in Artificial Intelligence models. In the proposed workflow, we consider from a set of models contained in a model repository, the computation of a trustworthy index obtained from the prediction explainability in both training and testing phases. The latter is to consider the most reliable model from the trustworthy index point of view.

Over the years, the number of compromised accounts dramatically increased. To avoid this type of attack various types of authentication methods were introduced. In particular, currently, researchers are focusing on biometric-based techniques such as physical-biometric and behavior-biometric. The idea at the bottom of the last technique is that each person exhibits a unique behavior. Starting from the touch dynamics, and keyboard dynamics nowadays, one of the most promising investigation areas is currently represented by mouse dynamics. Because of the simpler technology necessary to gather biometric data without employing user sensor data, the latter has recently been a popular study area. In this paper, we propose an approach for continuous and silent user authentication based on mouse dynamics and explainable deep learning. We build a set of images using an existing dataset of mouse dynamics in CSV format. The images obtained were then used to train a deep-learning model to discriminate between legitimate and malicious users. We also adopted the Gradient-weighted Class Activation Mapping, to allow highlighting the areas of the images which are responsible for a specific legitimate/attack prediction, thus providing explainability behind the model classification. The preliminary experimental analysis based on ten different users shows that the proposed method can be promising in silent and continuous user authentication.