A holistic review of cybersecurity and reliability perspectives in smart airports

Advances in the Internet of Things (IoT) and aviation sector have resulted in the emergence of smart airports. Services and systems powered by the IoT enable smart airports to have enhanced robustness, efficiency and control, governed by real-time monitoring and analytics. Smart sensors control the environmental conditions inside the airport, automate passenger-related actions and support airport security. However, these augmentations and automation introduce security threats to network systems of smart airports. Cyber-attackers demonstrated the susceptibility of IoT systems and networks to Advanced Persistent Threats (APT), due to hardware constraints, software flaws or IoT misconfigurations. With the increasing complexity of attacks, it is imperative to safeguard IoT networks of smart airports and ensure reliability of services, as cyber-attacks can have tremendous consequences such as disrupting networks, cancelling travel, or stealing sensitive information. There is a need to adopt and develop new Artificial Intelligence (AI)-enabled cyber-defence techniques for smart airports, which will address the challenges brought about by the incorporation of IoT systems to the airport business processes, and the constantly evolving nature of contemporary cyber-attacks. In this study, we present a holistic review of existing smart airport applications and services enabled by IoT sensors and systems. Additionally, we investigate several types of cyber defence tools including AI and data mining techniques, and analyse their strengths and weaknesses in the context of smart airports. Furthermore, we provide a classification of smart airport sub-systems based on their purpose and criticality and address cyber threats that can affect the security of smart airport\u27s networks

A holistic review of cybersecurity and reliability perspectives in smart airports

Advances in the Internet of Things (IoT) and aviation sector have resulted in the emergence of smart airports. Services and systems powered by the IoT enable smart airports to have enhanced robustness, efficiency and control, governed by real-time monitoring and analytics. Smart sensors control the environmental conditions inside the airport, automate passenger-related actions and support airport security. However, these augmentations and automation introduce security threats to network systems of smart airports. Cyber-attackers demonstrated the susceptibility of IoT systems and networks to Advanced Persistent Threats (APT), due to hardware constraints, software flaws or IoT misconfigurations. With the increasing complexity of attacks, it is imperative to safeguard IoT networks of smart airports and ensure reliability of services, as cyber-attacks can have tremendous consequences such as disrupting networks, cancelling travel, or stealing sensitive information. There is a need to adopt and develop new Artificial Intelligence (AI)-enabled cyber-defence techniques for smart airports, which will address the challenges brought about by the incorporation of IoT systems to the airport business processes, and the constantly evolving nature of contemporary cyber-attacks. In this study, we present a holistic review of existing smart airport applications and services enabled by IoT sensors and systems. Additionally, we investigate several types of cyber defence tools including AI and data mining techniques, and analyse their strengths and weaknesses in the context of smart airports. Furthermore, we provide a classification of smart airport sub-systems based on their purpose and criticality and address cyber threats that can affect the security of smart airport\u27s networks

A holistic review of cybersecurity and reliability perspectives in smart airports

Advances in the Internet of Things (IoT) and aviation sector have resulted in the emergence of smart airports. Services and systems powered by the IoT enable smart airports to have enhanced robustness, efficiency and control, governed by real-time monitoring and analytics. Smart sensors control the environmental conditions inside the airport, automate passenger-related actions and support airport security. However, these augmentations and automation introduce security threats to network systems of smart airports. Cyber-attackers demonstrated the susceptibility of IoT systems and networks to Advanced Persistent Threats (APT), due to hardware constraints, software flaws or IoT misconfigurations. With the increasing complexity of attacks, it is imperative to safeguard IoT networks of smart airports and ensure reliability of services, as cyber-attacks can have tremendous consequences such as disrupting networks, cancelling travel, or stealing sensitive information. There is a need to adopt and develop new Artificial Intelligence (AI)-enabled cyber-defence techniques for smart airports, which will address the challenges brought about by the incorporation of IoT systems to the airport business processes, and the constantly evolving nature of contemporary cyber-attacks. In this study, we present a holistic review of existing smart airport applications and services enabled by IoT sensors and systems. Additionally, we investigate several types of cyber defence tools including AI and data mining techniques, and analyse their strengths and weaknesses in the context of smart airports. Furthermore, we provide a classification of smart airport sub-systems based on their purpose and criticality and address cyber threats that can affect the security of smart airport\u27s networks

Lifestyles and socio-cultural factors among children aged 6-8 years from five Italian towns: The MAPEC-LIFE study cohort

Background: Lifestyles profoundly determine the quality of an individual’s health and life since his childhood. Many diseases in adulthood are avoidable if health-risk behaviors are identified and improved at an early stage of life. The aim of the present research was to characterize a cohort of children aged 6–8 years selected in order to perform an epidemiological molecular study (the MAPEC_LIFE study), investigate lifestyles of the children that could have effect on their health status, and assess possible association between lifestyles and socio-cultural factors. Methods: A questionnaire composed of 148 questions was administered in two different seasons to parents of children attending 18 primary schools in five Italian cities (Torino, Brescia, Pisa, Perugia and Lecce) to obtain information regarding the criteria for exclusion from the study, demographic, anthropometric and health information on the children, as well as some aspects on their lifestyles and parental characteristics. The results were analyzed in order to assess the frequency of specific conditions among the different seasons and cities and the association between lifestyles and socio-economic factors. Results: The final cohort was composed of 1,164 children (50.9 boys, 95.4% born in Italy). Frequency of some factors appeared different in terms of the survey season (physical activity in the open air, the ways of cooking certain foods) and among the various cities (parents’ level of education and rate of employment, sport, traffic near the home, type of heating, exposure to passive smoking, ways of cooking certain foods). Exposure to passive smoking and cooking fumes, obesity, residence in areas with heavy traffic, frequency of outdoor play and consumption of barbecued and fried foods were higher among children living in families with low educational and/or occupational level while children doing sports and consuming toasted bread were more frequent in families with high socio-economic level. Conclusions: The socio-economic level seems to affect the lifestyles of children enrolled in the study including those that could cause health effects. Many factors are linked to the geographical area and may depend on environmental, cultural and social aspects of the city of residence

Internet of things enabled policing processes

Theoretical thesis.Bibliography: pages 46-52.1 Introduction -- 2 Background and state-of-the-art -- 3 Enabling IoT platforms in data-driven knowledge-intensive processes -- 4 Experiment and evaluation -- 5 Conclusion and future directions.The Internet of Things (IoT) has the potential to transform many industries. This includes harnessing real-time intelligence to improve risk-based decision making and supporting adaptive processes from core to edge. For example, modern police investigation processes are often extremely complex, data-driven and knowledge-intensive. In such processes, it is not sufficient to focus on data storage and data analysis; as the knowledge workers (e.g., police investigators) will need to collect, understand and relate the big data (scattered across various systems) to process analysis. In this thesis, we analyze the state of the art in knowledge-intensive and data-driven processes. We present a scalable and extensible IoT-enabled process data analytics pipeline to enable analysts ingest data from IoT devices, extract knowledge from this data and link them to process execution data. We focus on a motivating scenario in policing, where a criminal investigator will be augmented by smart devices to collect data and to identify devices around the investigation location, to communicate with them to understand and analyze evidence. We design and implement a system (namely iCOP, IoT-enabled COP) to assist investigators collect large amounts of evidence and dig for the facts in an easy way.1 online resource (viii, 52 pages) colour illustration

Geological risks in large cities: The landslides triggered in the city of Rome (Italy) by the rainfall of 31 January-2 February 2014

An exceptional rainfall battered the city of Rome (Italy) from 31 January to 2 February 2014. The event had variable intensity and duration in the different parts of the city. The exceptionality of the event lies in the intensity of rainfall cumulated in 6 hours (return period > 50 years) and in its uneven distribution over the urban area. The event triggered a number of landslides of different type, which caused substantial damage. Researchers from the Centro di Ricerca per i Rischi Geologici (Research Centre on Prediction, Prevention and Control of Geological Risks - CERI) of the University of Rome "Sapienza" carried out field surveys and assessments immediately after the event. The team detected and inventoried 68 landslides, mostly occurring in the sandy and sandy-silty deposits of the Monte Mario, Ponte Galeria and Valle Giulia Formations. The complete inventory of the landslides is accessible via WebGIS on CERI's website http://www.ceri.uniroma1.it/cn/landslidesroma.jsp. The spatial distribution of the landslides evidences that 69% occurred in clastic deposits of sedimentary origin and only 6% in volcanic deposits. This finding disagrees with more general statistical data, based on the inventory of Rome's historical landslides, indicating that almost 41% of slope instabilities occur in volcanic deposits and almost 12% in sedimentary ones. In the data reported here, this apparent contradiction is justified by the fact that most the rainfall under review was concentrated in the north-western portion of Rome's urban area, whose hills accommodate outcrops of dominantly sedimentary deposits from Plio-Pleistocene marine and continental cycles. © Sapienza Università Editrice

Ground effects triggered by the 24th August 2016, Mw 6.0 Amatrice (Italy) earthquake. Surveys and inventoring to update the CEDIT catalogue

The CEDIT catalogue, Italian acronym of Catalogue of Earthquake-Induced Ground Effects, is available since 2011. After the Mw 6.0 Amatrice (Italy) earthquake (occurred at 01:36:32 UTC on 24th August, 2016) this catalogue was updated with 147 new inventoried ground effects. Since the first hours after the mainshock, field works and targeted remote sensing analyses were performed for recognizing and inventorying earthquake-induced ground effects. To avoid an inextricable overlap of ground effects due to either earthquake or rainfall events, intensive field activities were carried out and completed within a week, hence before the first intense rainfalls occurred on 30th of August. Ground effects mainly consist of landslides, in particular rock-falls and rock- and debris-slides, whereas less than 2% of the effects consist of ground cracks not directly related to landslides. The maximum distance from the epicenter of the surveyed ground effects is about 36 km, though more than 50% of the effects occurred within 20 km. The plano-altimetric distribution of ground effects is rather conditioned by the presence of road cuts, as well as by local natural hillslope topographic and morphological setting. The 73% of the triggered landslides intercepted road-cuts and accounted for significant interference with local traffic and emergency activities. The altimetric distribution of the ground effects covers a range of about 1000 m (from 600 up to 1600 m a.s.l.), emphasising that the ground effects involved the outcropping rock masses in different topographic conditions. Moreover, the homogeneous distribution of the ground effects into the different outcropping lithological units suggests that lithology did not play a principal role as predisposing factor for the earthquake-induced slopes failures occurred in the area. This work presents the methodological approach used for efficiently recognizing and inventorying ground effects triggered by the 24th August 2016 (Mw 6.0) Amatrice earthquake, as well as for managing and sharing results online on a global, pre-existing and public geo-database