In today’s data-driven world, the interconnection and automation of daily processes have become essential. As the demand for Internet connectivity grows, so does the need for robust cybersecurity measures. Operational Technology (OT), pivotal in controlling critical infrastructures such as power plants and water distribution systems, remains highly vulnerable. Many OT systems still rely on ‘air gaps’ for security, a measure increasingly insufficient as more systems connect to the internet for remote operation and data analysis. This article addresses the critical need for enhanced OT security solutions by introducing a novel tool focused on intelligent systems for the effective detection of cyber-attacks. The tool automates the creation of attack graphs and extracts attack paths from a JSON file describing the OT network. Leveraging the MulVAL attack graph generation engine, it provides a comprehensive visualization of potential attack vectors, enhancing the capability to identify and mitigate security threats in OT environments.

Despite of the global environmental crisis with record-high CO2 levels, urgent climate action is imperative. The EU’s ambitious emission reduction targets and the goal of climate neutrality by 2050 underline the severity of the situation. The Italy case study encounters challenges aligning with these objectives, necessitating significant emission cuts despite advancements in renewable energy and reduced energy consumption. Renewable Energy Communities (RECs) emerge as vital players, focusing on local production, consumption, and management of electrical energy. Our research introduces the GAIA federated software metaplatform, addressing the lack of multi-energy vector management by integrating diverse Internet-of-Things (IoT) software infrastructures. It simplifies the development of multi-energy vector services by amalgamating data from federated simple vector IoT infrastructures. GAIA aims to bridge the information gap on resource consumption and interconnections, benefiting RECs citizens and service providers. The platform enhances transparency, facilitating informed decision-making for REC stakeholders and it provides new opportunities and perspectives.

LORAWINE – Il Vino Si Fa in Vigna

A IoT platform for precisely tracing the entire production and transformation chain in vine sector.

With the growth of tourism industry, airplanes have became an affordable choice for medium- and long-distance travels. Accurate forecasting of flights tickets helps the aviation industry to match demand, supply flexibly and optimize aviation resources. Airline companies use dynamic pricing strategies to determine the price of airline tickets to maximize profits. Passengers want to purchase tickets at the lowest selling price for the flight of their choice. However, airline tickets are a special commodity that is time-sensitive and scarce, and the price of airline tickets is affected by various factors.Our research work provides a systematic comparison of various traditional machine learning methods (i.e., Ridge Regression, Lasso Regression, K-Nearest Neighbor, Decision Tree, XGBoost, Random Forest) and deep learning methods (e.g., Fully Connected Networks, Convolutional Neural Networks, Transformer) to address the problem of airfare prediction, by keeping the consumers’ needs. Moreover, we proposed innovative Bayesian neural networks, which represent the first exploitation attempt of Bayesian Inference for the airfare prediction task, to the best of our knowledge. Therefore, we evaluate the performance of our implemented and optimized models on an open dataset. The experimental results show that deep learning-based methods achieve better results on average than traditional ones, while Bayesian neural networks can achieve better performance among the other machine learning methods. However, taking into account both prediction performance and computational time, the Random Forest turns out to be the best choice to apply in this scenario.