In an era where industrial Security Operations Centers (SOCs) are paramount to enabling cybersecurity, they can unintentionally become enablers of intellectual property theft through the data they analyze and retain. The above issue requires finding solutions to strike a balance between data protection and security. This paper proposes a real-time data anonymization framework designed to operate directly within network devices. Using an extensive case study, our approach demonstrates how valuable intellectual property associated with industrial processes can be protected without compromising the effectiveness of behavioral anomaly detection systems. The methodology is designed to be nonintrusive, reversible, and seamlessly portable on existing security solutions. We evaluated these properties through comprehensive experimental testing, which showed both the method's effectiveness in securing intellectual property and its suitability for continuous real-time operation.

Accurately determining the number of people affected by emergencies is essential for deploying effective response measures during disasters. Traditional solutions like cellular and Wi-Fi networks are often rendered ineffective during such emergencies due to widespread infrastructure damage or non-functional connectivity, prompting the exploration of more resilient methods. This paper proposes a novel solution utilizing Bluetooth Low Energy (BLE) technology and decentralized networks composed entirely of mobile and wearable devices to count individuals autonomously without reliance on external communication equipment or specialized personnel. This count leverages uncoordinated relayed communication among devices within these networks, enabling us to extend our counting capabilities well beyond the direct range of rescuers. A formally evaluated, experimentally validated, and privacy-preserving counting algorithm that demonstrates rapid convergence and high accuracy even in large-scale scenarios is employed.

Radar systems have long been essential for safe navigation in various transportation sectors, including aviation, maritime, and automotive. While these systems provide invaluable situational awareness and decision-making capabilities, they increasingly become targets for malicious actors aiming to disrupt their normal operations. Electronic countermeasures (ECM) have traditionally been the predominant form of attack. However, recent findings have uncovered their vulnerability to cyber-based actions, capitalizing on their digitization and network connectivity. In this paper, we propose a novel threat model that exploits cyber attack capabilities against radar systems to simulate the effects of ECM. This model goes beyond known attacks by introducing a deceptive element, challenging attribution. To evaluate the feasibility of these attacks, extensive experimentation is conducted using a realistic case study involving the maritime domain. Through this research, we aim to highlight the evolving threats facing radar systems and the need for comprehensive security measures.

The rising of the Cyber-Physical System (CPS) and the Industry 4.0 paradigms demands the design and implementation of Digital Twin Frameworks (DTFs) that may support the quick build of reliable Digital Twins (DTs) for experimental and testing purposes. Most of the current DTF proposals allow the generation of DTs at a good pace but affect generality, scalability, portability, and completeness. As a consequence, current DTF are mostly domain-specific and hardly span several application domains (e.g., from simple IoT deployments to the modeling of complex critical infrastructures). Furthermore, the generated DTs often requires a high amount of computational resource to run. In this paper, we present LiDiTE, a solution based on a novel reference model for general-purpose DTFs. LiDiTE overcomes the limitations of state-of-the-art tools by supporting the fine-grained development of real-world complexity scenarios. To achieve that, LiDiTE builds on technologies that favor scalability, reuse, and extensibility of scenarios. We show such features by building the DT of real critical infrastructure and evaluating the performance of our DT against those of the real system. Further contributions of this paper include open access to the source code of LiDiTE and the experimental dataset.

The operation of radar equipment is one of the key facilities navigators use to gather situational awareness about their surroundings. With an ever-increasing need for always-running logistics and tighter shipping schedules, operators rely more on computerized instruments and their indications. As a result, modern ships have become complex cyber-physical systems in which sensors and computers constantly communicate and coordinate. In this work, we discuss novel threats related to the radar system, one of a ship’s most security-sensitive components. In detail, we first discuss some new attacks capable of compromising the integrity of data displayed on a radar system, with potentially catastrophic impacts on the crew’s situational awareness or safety. Then, we present a detection system to highlight anomalies in the radar video feed, requiring no modifications to the target ship configuration. Finally, we stimulate our detection system by performing the attacks inside a simulated environment. The experimental results indicate that the attacks are feasible, easy to carry out, and hard to detect. Moreover, they prove that the proposed detection technique is effective.

This paper uses data processing techniques to reduce the required transmission bandwidth in ship-to-shore communications. The proposed framework (ONline Efficient Sources Transmission Optimizer - ONESTO) leverages state-of-the-art technologies and novel algorithms to automatically optimize transmissions under structural (e.g., available bandwidth, fixed packet overhead) and user-defined (e.g., maximum latency) constraints. In addition, ONESTO authenticates and encrypts the communication between the ship and the shore via mainstream free and open-source software components. Initially, we present the abstract mathematical formulation of the problem, with its assumptions, goal function, constraints, and significant quantities. Then, we introduce the architecture of a system capable of continuously estimating the compressibility, processing and transmission time of streaming data. Such estimations allow ONESTO to calculate and apply optimal parameters for achieving the best compression ratio. Lastly, using a prototypical implementation, we evaluate the system performance with a Class B ship simulator on two realistic use cases. Our experiments show an excellent compression ratio with maritime protocols (more than 40:1) and a limited latency impact, demonstrating the approach’s viability.