Nowadays,the use of digital technologies is promoting three main characteristics of information, i.e. the volume, the modality and the frequency. Due to the amount of information generated by tools and individuals, it has been identified a critical need for the Law Enforcement Agencies to exploit this information and carry out criminal investigations in an effective way. To respond to the increasing challenges of managing huge amounts of heterogeneous data generated at high frequency, the paper outlines a modular approach adopted for the processing of information gathered from different information sources, and the extraction of knowledge to assist criminal investigation. The proposed platform provides novel technologies and efficient components for processing multimedia information in a scalable and distributed way, allowing Law Enforcement Agencies to make the analysis and a multidimensional visualization of criminal information in a single and secure point.

This paper evaluates the technical feasibility of Distributed Ledger Technology (DLT) within the healthcare ecosystem, with a focus on the use of Corda DLT to enhance governance and performance in a decentralized ecosystem, ensuring data integrity, security, and trustworthiness. Key attributes examined include the guarantee of data integrity, ensuring that transmitted data remain unaltered; authenticity through the implementation of digital signatures and certificates; confidentiality achieved via secure peer-to-peer communication accessible only to authorized parties; and traceability and auditing mechanisms that enable tracking of information changes and accountability. To validate these features, a Corda Distributed Application (CorDapp) was developed to manage the core logic of the healthcare ecosystem. The CorDapp was deployed across nodes and executed within the Corda network. Its performance was assessed using metrics such as throughput, latency, CPU usage, and memory consumption in both local and cloud network environments. Results demonstrate the feasibility of using Corda DLT technology in healthcare, effectively addressing critical requirements such as integrity, authenticity, confidentiality, traceability, and auditing while maintaining satisfactory performance across diverse deployment scenarios.

Cyberattacks, which consist of exploiting security vulnerabilities of computer networks and systems for any kind of malicious purpose (e.g., extortion, data steal, assets hijacking), have been continuously increasing worldwide in recent years. Cyberspace appears today as a new battlefield, along with physical world scenarios (land, sea, air, and space), for the organizations defence and security. Besides, by the fact that attacks from the physical world may have significant implications in the cyber world and vice versa, these dimensions cannot be understood independently. However, the most common intelligence systems offer an insufficient situational awareness exclusively focused on one of these decision spaces. This article introduces HYBINT, an enhanced intelligence system that provides the necessary decision-making support for an efficient critical infrastructures protection by combining the real-time situation of the physical and cyber domains in a single visualization space. HYBINT is a real cross-platform solution which supplies, through Big Data analytical methods and advanced representation techniques, hybrid intelligence information from significant data of both physical and cyber data sources in order to bring an adequate hybrid situational awareness (HSA) of the cyber-physical environment. The proposal will be validated in a detailed scenario in which HYBINT system will be evaluated.

To successfully confront a disaster, coordinated and collaborative participation of multiple agencies related to public safety is essential by ensuring a response consistent with the requirements of the emergency environment and of those affected. Therefore, permanent information exchange between the agencies involved becomes mandatory, thus enabling them to combine their efforts towards best facing the emergency. This article describes an interoperability platform architecture which allows the exchange of information between the agencies concerned in the management of an emergency through the use of their own information systems and computer tools. The architecture core resides inside the Shared Information Space, in charge of managing the information from the systems integrated within the platform as a single storage entity. It is founded on a non-relational distributed database and a P2P communications network devised to share out the workload between all the platform nodes, thus conferring availability and scalability to the architecture.

This paper presents a cloud-based architecture for the acquisition, transmission, and processing of acoustic data from hydrophone arrays, designed to enable the detection and monitoring of both surface and underwater vehicles. The proposed system offers a modular and scalable cloud infrastructure that supports real-time and distributed processing of hydrophone data collected in diverse aquatic environments. Acoustic signals captured by heterogeneous hydrophones—featuring varying sensitivity and bandwidth—are streamed to the cloud, where several machine learning algorithms can be deployed to extract distinguishing acoustic signatures from vessel engines and propellers in interaction with water. The architecture leverages cloud-based services for data ingestion, processing, and storage, facilitating robust vehicle detection and localization through propagation modeling and multi-array geometric configurations. Experimental validation demonstrates the system’s effectiveness in handling high-volume acoustic data streams while maintaining low-latency processing. The proposed approach highlights the potential of cloud technologies to deliver scalable, resilient, and adaptive acoustic sensing platforms for applications in maritime traffic monitoring, harbor security, and environmental surveillance.