Airports are at the forefront of technological innovation, mainly due to the fact that the number of air travel passengers is exponentially increasing every year. As a result, airports enhance their infrastructure intelligence and evolve as smart facilities to support growth, by offering an enjoyable travel experience. New challenges are coming up, which aviation has to deal with and adapt to, such as the integration of Industrial IoT (Internet of Things) in airport facilities and the increased use of smart devices from travelers and employees. Cybersecurity is becoming a key enabler for safety, which is paramount in the aviation context. Smart airports strive to provide optimal services in a reliable and sustainable manner, by working around the domains of growth, efficiency, safety and security. This article researches: (a) the implementation rate of cybersecurity measures in commercial airports; (b) malicious threats that evolve due to IoT and smart devices installed; (c) risk scenario analysis for IoT malicious attacks with threat mitigation actions. With the aim to enhance operational practices and develop robust cybersecurity governance in smart airports, we present a systematic and comprehensive analysis of malicious attacks in smart airports, to facilitate airport community comprehend risks and proactively act, by implementing cybersecurity best practices and resilience measures.

The world has been afflicted by the rise of misinformation. The sheer volume of news produced daily necessitates the development of automated methods for separating fact from fiction. To tackle this issue, the computer science community has produced a plethora of approaches, documented in a number of surveys. However, these surveys primarily rely on one-dimensional solutions, i.e., deception detection approaches that focus on a specific aspect of misinformation, such as a particular topic, language, or source. Misinformation is considered a major obstacle for situational awareness, including cyber, both from a company and a societal point of view. This paper explores the evolving field of misinformation detection and analytics on information published in news articles, with an emphasis on methodologies that handle multiple dimensions of the fake news detection conundrum. We analyze and compare existing research on cross-dimensional methodologies. Our evaluation process is based on a set of criteria, including a predefined set of performance metrics, data pre-processing features, and domains of implementation. Furthermore, we assess the adaptability of each methodology in detecting misinformation in real-world news and thoroughly analyze our findings. Specifically, survey insights demonstrate that when a detection approach focuses on several dimensions (e.g., languages and topics, languages and sources, etc.), its performance improves, and it becomes more flexible in detecting false information across different contexts. Finally, we propose a set of research directions that could aid in furthering the development of more advanced and accurate models in this field.

Industrial Internet of Things (IIoT) refers to a broad network of low-cost, interconnected devices, including actuators, programmable logic controllers (PLCs), and sensors. Such environments are characterized by the vast amount of data exchanged among a wide range of devices, applications, and services. The scalability and decentralized nature of IIoT introduces considerable challenges for traditional security mechanisms. As a result, it is crucial to establish more robust security measures, enforce more effective access control policies, and efficiently manage information flows within business processes. In our prior research, we introduced a methodology for the assessment of information flows in IIoT environments and the detection of the illegal ones. Specifically, we utilized a risk-based methodology to model complex business processes as directed graphs. This approach enabled us to thoroughly analyze the interdependencies among participating objects. Through this analysis, we aimed to identify objects that are susceptible to initiating or being influenced by illegal information flows. In our current study, we investigate the propagation of the risk of illegal information flows within and across business processes. Finally, we apply centrality metrics to identify critical objects that require more efficient access control rules and policies in order to mitigate illegal information flows within the IIoT network. To the best of our knowledge, no previous research has explored the concept of risk-based detection of illegal information flows and examined potential propagation of risk in industrial environments.