Engage D1.2 Final Project Results Report

This deliverable summarises the activities and results of Engage, the SESAR 2020 Knowledge Transfer Network (KTN). The KTN initiated and supported multiple activities for SESAR and the European air traffic management (ATM) community, including PhDs, focused catalyst fund projects, thematic workshops, summer schools and the launch of a wiki as the one-stop, go-to source for ATM research and knowledge in Europe. Key throughout was the integration of exploratory and industrial research, thus expediting the innovation pipeline and bringing researchers together. These activities laid valuable foundations for the SESAR Digital Academy

Improving resilience in Critical Infrastructures through learning from past events

Modern societies are increasingly dependent on the proper functioning of Critical Infrastructures (CIs). CIs produce and distribute essential goods or services, as for power transmission systems, water treatment and distribution infrastructures, transportation systems, communication networks, nuclear power plants, and information technologies. Being resilient, where resilience denotes the capacity of a system to recover from challenges or disruptive events, becomes a key property for CIs, which are constantly exposed to threats that can undermine safety, security, and business continuity. Nowadays, a variety of approaches exists in the context of CIs’ resilience research. This dissertation starts with a systematic review based on PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) on the approaches that have a complete qualitative dimension, or that can be used as entry points for semi-quantitative analyses. The review identifies four principal dimensions of resilience referred to CIs (i.e., techno-centric, organizational, community, and urban) and discusses the related qualitative or semi-quantitative methods. The scope of the thesis emphasizes the organizational dimension, as a socio-technical construct. Accordingly, the following research question has been posed: how can learning improve resilience in an organization? Firstly, the benefits of learning in a particular CI, i.e. the supply chain in reverse logistics related to the small arms utilized by Italian Armed Forces, have been studied. Following the theory of Learning From Incidents, the theoretical model helped to elaborate a centralized information management system for the Supply Chain Management of small arms within a Business Intelligence (BI) framework, which can be the basis for an effective decision-making process, capable of increasing the systemic resilience of the supply chain itself. Secondly, the research question has been extended to another extremely topical context, i.e. the Emergency Management (EM), exploring the crisis induced learning where single-loop and double-loop learning cycles can be established regarding the behavioral perspective. Specifically, the former refers to the correction of practices within organizational plans without changing core beliefs and fundamental rules of the organization, while the latter aims at resolving incompatible organizational behavior by restructuring the norms themselves together with the associated practices or assumptions. Consequently, with the aim of ensuring high EM systems resilience, and effective single-loop and double-loop crisis induced learning at organizational level, the study examined learning opportunities that emerge through the exploration of adaptive practices necessary to face the complexity of a socio-technical work domain as the EM of Covid-19 outbreaks on Oil & Gas platforms. Both qualitative and quantitative approaches have been adopted to analyze the resilience of this specific socio-technical system. On this consciousness, with the intention to explore systems theoretic possibilities to model the EM system, the Functional Resonance Analysis Method (FRAM) has been proposed as a qualitative method for developing a systematic understanding of adaptive practices, modelling planning and resilient behaviors and ultimately supporting crisis induced learning. After the FRAM analysis, the same EM system has also been studied adopting a Bayesian Network (BN) to quantify resilience potentials of an EM procedure resulting from the adaptive practices and lessons learned by an EM organization. While the study of CIs is still an open and challenging topic, this dissertation provides methodologies and running examples on how systemic approaches may support data-driven learning to ultimately improve organizational resilience. These results, possibly extended with future research drivers, are expected to support decision-makers in their tactical and operational endeavors

Smart Cities: A Review and Analysis of Stakeholders’ Literature

Recent literature on smart cities stresses the role of digitization in tackling urban issues such as environmental degradation and poverty. The wicked nature of these issues gives rise to the need to understand the diverse perspectives of relevant stakeholder groups on smart cities. However, existing research that compares these perspectives tends to exclude the beliefs of those living in smart cities. Integrating these beliefs in smart city discourses is paramount to increase the likelihood that these systems will be accepted. With the view that the literature consumed by an audience will influence that audience’s perspectives, the main aim of this study is to compare and contrast the pertinent topics found in various types of literature on smart cities. Using an innovative approach of literature comparison, based on a semantic entity annotator and keyword analysis, this article extracts and compares topics in news media (for citizens), trade publications (for businesses), academic articles (for research organizations) and government reports (for governments). The findings suggest that citizens tend to be under-represented in discussions on smart cities and highlight those topics considered relevant only by smart city citizens. Increased understanding in this area can help guide discussions and policies that are relevant for all stakeholders

Covid19/IT the digital side of Covid19: A picture from Italy with clustering and taxonomy

The Covid19 pandemic has significantly impacted on our lives, triggering a strong reaction resulting in vaccines, more effective diagnoses and therapies, policies to contain the pandemic outbreak, to name but a few. A significant contribution to their success comes from the computer science and information technology communities, both in support to other disciplines and as the primary driver of solutions for, e.g., diagnostics, social distancing, and contact tracing. In this work, we surveyed the Italian computer science and engineering community initiatives against the Covid19 pandemic. The 128 responses thus collected document the response of such a community during the first pandemic wave in Italy (February-May 2020), through several initiatives carried out by both single researchers and research groups able to promptly react to Covid19, even remotely. The data obtained by the survey are here reported, discussed and further investigated by Natural Language Processing techniques, to generate semantic clusters based on embedding representations of the surveyed activity descriptions. The resulting clusters have been then used to extend an existing Covid19 taxonomy with the classification of related research activities in computer science and information technology areas, summarizing this work contribution through a reproducible survey-to-taxonomy methodology

Engage D3.10 Research and innovation insights

Engage is the SESAR 2020 Knowledge Transfer Network (KTN). It is managed by a consortium of academia and industry, with the support of the SESAR Joint Undertaking. This report highlights future research opportunities for ATM. The basic framework is structured around three research pillars. Each research pillar has a dedicated section in this report. SESAR’s Strategic Research and Innovation Agenda, Digital European Sky is a focal point of comparison. Much of the work is underpinned by the building and successful launch of the Engage wiki, which comprises an interactive research map, an ATM concepts roadmap and a research repository. Extensive lessons learned are presented. Detailed proposals for future research, plus research enablers and platforms are suggested for SESAR 3

Detection of Software Vulnerability Communication in Expert Social Media Channels: A Data-driven Approach

Conceptually, a vulnerability is: A flaw or weakness in a system’s design, implementation,or operation and management that could be exploited to violate the system’s security policy .Some of these flaws can go undetected and exploited for long periods of time after soft-ware release. Although some software providers are making efforts to avoid this situ-ation, inevitability, users are still exposed to vulnerabilities that allow criminal hackersto take advantage. These vulnerabilities are constantly discussed in specialised forumson social media. Therefore, from a cyber security standpoint, the information found inthese places can be used for countermeasures actions against malicious exploitation ofsoftware. However, manual inspection of the vast quantity of shared content in socialmedia is impractical. For this reason, in this thesis, we analyse the real applicability ofsupervised classification models to automatically detect software vulnerability com-munication in expert social media channels. We cover the following three principal aspects: Firstly, we investigate the applicability of classification models in a range of 5 differ-ent datasets collected from 3 Internet Domains: Dark Web, Deep Web and SurfaceWeb. Since supervised models require labelled data, we have provided a systematiclabelling process using multiple annotators to guarantee accurate labels to carry outexperiments. Using these datasets, we have investigated the classification models withdifferent combinations of learning-based algorithms and traditional features represen-tation. Also, by oversampling the positive instances, we have achieved an increaseof 5% in Positive Recall (on average) in these models. On top of that, we have appiiplied Feature Reduction, Feature Extraction and Feature Selection techniques, whichprovided a reduction on the dimensionality of these models without damaging the accuracy, thus, providing computationally efficient models. Furthermore, in addition to traditional features representation, we have investigated the performance of robust language models, such as Word Embedding (WEMB) andSentence Embedding (SEMB) on the accuracy of classification models. RegardingWEMB, our experiment has shown that this model trained with a small security-vocabulary dataset provides comparable results with WEMB trained in a very large general-vocabulary dataset. Regarding SEMB model, our experiment has shown thatits use overcomes WEMB model in detecting vulnerability communication, recording 8% of Avg. Class Accuracy and 74% of Positive Recall. In addition, we investigate twoDeep Learning algorithms as classifiers, text CNN (Convolutional Neural Network)and RNN (Recurrent Neural Network)-based algorithms, which have improved ourmodel, resulting in the best overall performance for our task

Personalized question-based cybersecurity recommendation systems

En ces temps de pandémie Covid19, une énorme quantité de l’activité humaine est modifiée pour se faire à distance, notamment par des moyens électroniques. Cela rend plusieurs personnes et services vulnérables aux cyberattaques, d’où le besoin d’une éducation généralisée ou du moins accessible sur la cybersécurité. De nombreux efforts sont entrepris par les chercheurs, le gouvernement et les entreprises pour protéger et assurer la sécurité des individus contre les pirates et les cybercriminels. En raison du rôle important joué par les systèmes de recommandation dans la vie quotidienne de l'utilisateur, il est intéressant de voir comment nous pouvons combiner les systèmes de cybersécurité et de recommandation en tant que solutions alternatives pour aider les utilisateurs à comprendre les cyberattaques auxquelles ils peuvent être confrontés. Les systèmes de recommandation sont couramment utilisés par le commerce électronique, les réseaux sociaux et les plateformes de voyage, et ils sont basés sur des techniques de systèmes de recommandation traditionnels. Au vu des faits mentionnés ci-dessus, et le besoin de protéger les internautes, il devient important de fournir un système personnalisé, qui permet de partager les problèmes, d'interagir avec un système et de trouver des recommandations. Pour cela, ce travail propose « Cyberhelper », un système de recommandation de cybersécurité personnalisé basé sur des questions pour la sensibilisation à la cybersécurité. De plus, la plateforme proposée est équipée d'un algorithme hybride associé à trois différents algorithmes basés sur la connaissance, les utilisateurs et le contenu qui garantit une recommandation personnalisée optimale en fonction du modèle utilisateur et du contexte. Les résultats expérimentaux montrent que la précision obtenue en appliquant l'algorithme proposé est bien supérieure à la précision obtenue en utilisant d'autres mécanismes de système de recommandation traditionnels. Les résultats suggèrent également qu'en adoptant l'approche proposée, chaque utilisateur peut avoir une expérience utilisateur unique, ce qui peut l'aider à comprendre l'environnement de cybersécurité.With the proliferation of the virtual universe and the multitude of services provided by the World Wide Web, a major concern arises: Security and privacy have never been more in jeopardy. Nowadays, with the Covid 19 pandemic, the world faces a new reality that pushed the majority of the workforce to telecommute. This thereby creates new vulnerabilities for cyber attackers to exploit. It’s important now more than ever, to educate and offer guidance towards good cybersecurity hygiene. In this context, a major effort has been dedicated by researchers, governments, and businesses alike to protect people online against hackers and cybercriminals. With a focus on strengthening the weakest link in the cybersecurity chain which is the human being, educational and awareness-raising tools have been put to use. However, most researchers focus on the “one size fits all” solutions which do not focus on the intricacies of individuals. This work aims to overcome that by contributing a personalized question-based recommender system. Named “Cyberhelper”, this work benefits from an existing mature body of research on recommender system algorithms along with recent research on non-user-specific question-based recommenders. The reported proof of concept holds potential for future work in adapting Cyberhelper as an everyday assistant for different types of users and different contexts