In today's interconnected cyber landscape, Distributed Denial of Service (DDoS) attacks represent a significant threat to the smooth functioning of online infrastructures. The nature of DDoS attacks, characterized by their distributed and dynamic nature, poses significant challenges for traditional centralized approaches to model training; however, the challenges of collaborative DDoS detection are compounded by stringent data privacy regulations, leaving mitigation efforts largely reliant on standalone and inflexible firewalls. Federated Learning (FL) represents a cutting-edge innovation in cybersecurity, presenting a revolutionary method for collectively training deep learning models without compromising sensitive data. Despite its promise, practical hurdles remain, particularly the reliance of most FL algorithms on centralized, server-side data for model evalu-ation-though some approaches avoid this centralized testing dependency. This limitation hinders the applicability of FL, especially in scenarios involving zero-day attacks on clients. Our paper examines a key hypothesis: whether the aggregated information from multiple clients can be effectively utilized to develop a global model that is inherently more resilient to zeroday attacks compared to models trained solely on individual client data. To investigate this, we introduce a methodology wherein FL models are trained on established DDoS attacks and subsequently evaluated against entirely novel, unencountered attacks, simulating zero-day scenarios at the client level. To ensure that each client contributes effectively to the training process, we utilize Jensen-Shannon Divergence (JSD) to evaluate and filter client updates based on their alignment with the global model. Building on this, we implement a kernel density estimation-based aggregation method to effectively mitigate feature distribution bias-a common issue in DDoS detection within FL environments. This approach forms a core component of our proposed framework, FLAME, which is built using the distributed framework Flower to realistically simulate FL in a decentralized setting. The code for our implementation can be found at: https://github.com/MODAL-UNINA/FLAME.

ABSTRACT Federated learning (FL) is gaining traction across numerous fields for its ability to foster collaboration among multiple participants while preserving data privacy. In the medical domain, FL enables institutions to share knowledge while maintaining control over their data, which often vary in modality, source, and quantity. Institutions are often specialised in treating one or a few types of tumours, typically focusing on a specific organ. Hence, different institutions may contribute with distinct types of medical imaging data of various organs, originating from diverse machines. Collaboration among these institutions enhances performance on shared tasks across different areas of the body. The framework employs modality‐specific models hosted on the server, each designed for a particular imaging modality and designed to predict the presence of tumours in scans from its respective modality, regardless of the organ being imaged. Clients focus on their specific imaging modality, utilising knowledge derived from images contributed by institutions employing the same modality. This approach facilitates broader collaboration, extending beyond institutions specialising in the same organ to include those working within the same imaging modality. This approach also helps avoid the introduction of potential noise from clients with images of different modalities, which might hinder the model's ability to effectively specialise and adapt to the data specific to each institution. Experiments showed that FLAMES achieves strong performance on server data, even when tested across different organs, demonstrating its ability to generalise effectively across diverse medical imaging datasets. Our code is available at https://github.com/MODAL‐UNINA/FLAMES .

Accurate energy consumption forecasting is becoming increasingly important due to rising global energy demands driven by economic development and population growth. Traditional forecasting models often overlook the impact of contextual factors, such as weather conditions and occupancy trends, which are essential for precise predictions. In this study, we propose a hybrid context-aware simulated scenario generation (CA-SSG) approach that integrates context space theory (CST) with deep learning techniques. This method leverages key contextual features to generate synthetic energy consumption data that more accurately mimics real-world patterns. Using the ASHRAE Great Energy Predictor III dataset, which includes diverse building types across various climates, we demonstrate the effectiveness of CA-SSG. The results show significant improvements in model performance, with reductions in Kullback-Leibler divergence (5%), increases in Pearson Correlation Coefficient (5%), and decreases in computation time compared to traditional approaches. These findings highlight the advantages of contextually enriched generative models for developing smarter energy management systems, enabling more accurate energy forecasting, and supporting strategic planning for energy consumption.

In the realm of edge cloud computing (ECC), Federated Learning (FL) revolutionizes the decentralization of machine learning (ML) models by enabling their training across multiple devices. In this way, FL preserves privacy and minimizes the need for centralized data by processing data near the source. From a communication standpoint, only the model weights are exchanged between devices. By avoiding the need to send data to a centralized location for processing, FL reduces the energy required for data transfer and supports more efficient use of computing resources at the edge. FL is particularly advantageous for resource-constrained devices, such as smartphones and IoT devices. However, this limited computational power and battery capacity and the challenge of energy consumption are critical aspects of FL systems. This paper introduces Eco-FL, an innovative methodology designed to optimize energy consumption in FL systems, in the field of Green Edge Cloud Computing (GECC). Our approach employs a device selection process that considers the entropy of the data held by the devices and their available energy reserves. This ensures that devices with lower energy availability are less likely to participate in the training rounds, prioritizing those with higher energy capacities. To evaluate the efficacy of our methodology, we utilize FedEntropy, an entropy-based aggregation method, alongside established aggregation methods such as FedAvg and FedProx for performance comparison. The effectiveness of Eco-FL in reducing energy consumption without compromising the accuracy of the FL process is demonstrated through analyses conducted on three distinct datasets. These analyses vary the β parameter of the Dirichlet distribution and account for scenarios with both homogeneous and heterogeneous initial device charges. Our findings validate Eco-FL’s potential to enhance the sustainability of FL systems by judiciously managing client participation based on energy criteria, presenting a significant step forward in the development of energy-efficient FL.

Network Intrusion Detection Systems (NIDS) are crucial tools for protecting networked devices from cyberattacks. Recent development in the field of Artificial Intelligence (AI) has provided tremendous advantages in implementing NIDSs able to monitor network traffic and block cyberattacks in real-time. In the literature, it is widely recognized that the effective training of a NIDS requires a large quantity of labeled traffic, representative of attacks. Nonetheless, the availability of public and abundant datasets remains remarkably restricted due to the cost of gathering and labeling real traffic traces and privacy concerns for sharing them. To tackle these challenges, in this paper we present a generative AI model capable of synthesizing anonymized traffic traces from real ones, thus dealing with privacy, abundance, and representativeness. The proposal is based on a Conditional Variational Autoencoder (CVAE) and a preprocessing procedure specifically designed for the generation of new traffic traces. To validate our solution, we conduct an extensive empirical study leveraging three recent and publicly-available datasets, containing benign and malicious traffic. The validation is carried out from both the perspectives of classification performance of a robust NIDS and the quality of synthetic data, in comparison to the utilization of real data. We compare our CVAE with two state-of-the-art AI-based traffic data generators and prove that, trained with traces emitted by our generative model, a NIDS has a limited F1-score loss compared to training on real data; competing models instead struggle or fail to generate traces that are as effective for NIDS training and as statistically similar to the original. We make the synthetic datasets available in both PCAP and tabular formats, to facilitate the reproducibility of our findings and encourage further exploration in the field of generative AI for networking.

Environment perception through deep computation in unstructured environments is important for the construction of autonomous navigation systems. Most research focuses on navigation in structured scenes, including indoor mobility and driving along roads, while neglecting to consider unstructured environments, which often contain diverse heights and distributions. In addition, existing depth estimation algorithms based on deep learning often need to complete training under the supervision of Ground truth, and GT data with a large number of labels are not always easy to obtain. To tackle this issue, this paper proposes an unsupervised stereo depth estimation method for processing UAV navigation images in an unstructured environment. The method contains a primitive U-shaped CNN network architecture for processing such scenes. The feature extraction layer of the network is based on the YOLOv3 residual structure, and additional attention modules help the network enhance its ability to perceive image features. Finally, depth estimation experiments on the unstructured environments dataset Mid-Air further demonstrate the effectiveness and reliability of the proposed method.

The Forward-forward (FF) algorithm is a new method for training neural networks, proposed as an alternative to the traditional Backpropagation (BP) algorithm by Hinton. The FF algorithm replaces the backward computations in the learning process with another forward pass. Each layer has an objective function, which aims to be high for positive data and low for negative ones. This paper presents a preliminary investigation into variations of the FF algorithm, such as incorporating a local Backpropagation to create a hybrid network that robustly converges while preserving the ability to avoid backward computations when needed, for example, in non-differentiable areas of the network. Additionally, a pseudo-random logic for selecting trainable stacks of layers at each epoch is proposed to speed up the learning process.

The advent of the digital innovation era is changing service, use, and resources management paradigms, offering a wide range of new and essential opportunities. In particular, the advent of the Internet of Things (IoT), i.e., the ability to connect individual objects to the Internet, also capable of communicating autonomously, has its particular declination on the connected vehicle. It is combined with the potential of advanced sensors placed pervasively on vehicles, which offer multifunctional monitoring capabilities of the entire system: from individual components up to the whole vehicle, including driver behavior and conditions and many exogenous parameters to the vehicle (road and weather conditions, congestion, risk situations, changes to mobility plans, etc.). In this perspective, machine learning (ML) models can transform raw data into new knowledge; they can contribute in an innovative way to define and suggest decisions, strategies, and criteria for resource use. Nowadays, most intelligent mobility projects also integrate artificial intelligence (AI) and ML solutions. In this article, we present and discuss the application of unsupervised learning techniques on a vehicular IoT data set. The main goal is to generate new knowledge about a geographical zone by analyzing historical drivers behavioral data. The autonomous vehicle’s framework can exploit the generated valuable insights to optimize the routes and prevent critical issues.

New technologies are transforming medicine, and this revolution starts with data. Usually, health services within public healthcare systems are accessed through a booking centre managed by local health authorities and controlled by the regional government. In this perspective, structuring e-health data through a Knowledge Graph (KG) approach can provide a feasible method to quickly and simply organize data and/or retrieve new information. Starting from raw health bookings data from the public healthcare system in Italy, a KG method is presented to support e-health services through the extraction of medical knowledge and novel insights. By exploiting graph embedding which arranges the various attributes of the entities into the same vector space, we are able to apply Machine Learning (ML) techniques to the embedded vectors. The findings suggest that KGs could be used to assess patients' medical booking patterns, either from unsupervised or supervised ML. In particular, the former can determine possible presence of hidden groups of entities that is not immediately available through the original legacy dataset structure. The latter, although the performance of the used algorithms is not very high, shows encouraging results in predicting a patient's likelihood to undergo a particular medical visit within a year. However, many technological advances remain to be made, especially in graph database technologies and graph embedding algorithms.

Nowadays, predictive medicine begins to become a reality thanks to Artificial Intelligence (AI) which allows, through the processing of huge amounts of data, to identify correlations not perceptible to the human brain. The application of AI in predictive diagnostics is increasingly pervasive; through the use and interpretation of data, the first signs of some diseases (i.e. tumours) can be detected to help physicians make more accurate diagnoses to reduce the errors and develop methods for individualized medical treatment. In this perspective, salivary gland tumours (SGTs) are rare cancers with variable malignancy representing less than 1% of all cancer diagnoses and about 5% of head and neck cancers. The clinical management of SGTs is complicated by a high rate of preclinical diagnostic errors. Today, fine needle aspiration cytology (FNAC) represents the primary diagnostic tool in the hands of clinicians. However, it provides information that about 25% of cases are dubious or inconclusive, complicating therapeutic choices. Thus, finding new tools supporting clinicians to make the right choices in doubtful cases is necessary. This research work presents and discusses a Deep Learning-based framework for automatic segmentation and classification of salivary gland tumours. Furthermore, we propose an explainable segmentation learning approach supporting the effectiveness of the proposed framework through a per-epoch learning process analysis and the attention map mechanism. The proposed framework was evaluated with a collected CT dataset of patients with salivary gland tumours. Experimental results show that our methodology achieves significant scores on both segmentation and classification tasks.

Abstract Slope deformation prediction is crucial for early warning of slope failure, which can prevent property damage and save human life. Existing predictive models focus on predicting the displacement of a single monitoring point based on time series data, without considering spatial correlations among monitoring points, which makes it difficult to reveal the displacement changes in the entire monitoring system and ignores the potential threats from nonselected points. To address the above problem, this paper presents a novel deep learning method for predicting the slope deformation, by considering the spatial correlations between all points in the entire displacement monitoring system. The essential idea behind the proposed method is to predict the slope deformation based on the global information (i.e., the correlated displacements of all points in the entire monitoring system), rather than based on the local information (i.e., the displacements of a specified single point in the monitoring system). In the proposed method, (1) a weighted adjacency matrix is built to interpret the spatial correlations between all points, (2) a feature matrix is assembled to store the time-series displacements of all points, and (3) one of the state-of-the-art deep learning models, i.e., T-GCN, is developed to process the above graph-structured data consisting of two matrices. The effectiveness of the proposed method is verified by performing predictions based on a real dataset. The proposed method can be applied to predict time-dependency information in other similar geohazard scenarios, based on time-series data collected from multiple monitoring points.

Nowadays, a sustainable and smart city focuses on energy efficiency and the reduction of polluting emissions through smart mobility projects and initiatives to “sensitize” infrastructure. Smart parking is one of the building blocks of intelligent mobility, innovative mobility that aims to be flexible, integrated, and sustainable and consequently integrated into a Smart City. By using the Internet of Things (IoT) sensors located in the parking areas or the underground car parks in combination with a mobile application, which indicates to citizens the free places in the different areas of the city and guides them toward the chosen parking, it is possible to reduce air pollution and fluidifying noise traffic. In this article, we present and discuss an innovative Deep Learning-based ensemble technique in forecasting the parking space occupancy to reduce the search time for parking and to optimize the flow of cars in particularly congested areas, with an overall positive impact on traffic in urban centres. A genetic algorithm has also been used to optimize predictors parameters. The main goal is to design an intelligent IoT-based service that can predict, in the next few hours, the parking spaces occupancy of a street. The proposed approach has been assessed on a real IoT dataset composed by over than 15M of collected sensor records. Obtained results demonstrate that our method outperforms both single predictors and the widely used strategy of the mean providing inherently robust predictions.