The evidence review report from SAPEA presents the latest scientific evidence on the subject of crisis management, and evidence-based policy options for action. The European Union is confronted with an increasing number of crises with growing complexity. Their effects can cascade and ripple to all parts of society, the economy, and the environment. The effects of such crises are not limited to specific geographical regions or sectors of society, and many crises continue indefinitely. Because they last, they overlap and amplify each other. Decisions made during crises are not only urgent, but have long-lasting consequences. Therefore, strategic crisis management needs to be aligned with broader policy objectives. Using digital tools, guidance and standards can be set for strategic foresight and risk monitoring, to ensure that a longer-term perspective is integrated. Better data preparedness can ensure that information can be shared effectively while respecting privacy and security when a crisis hits. To address the growing complexity of crises, we need a vision that integrates both risk management and crisis management. Different activities and organisations also need to coordinate: responses on multiple geographical and temporal scales need to be aligned, rather than aiming at the one ‘right’ level. Here, the EU could play an important coordinating role. The EU can also play a role in training decision- and policy-makers to coordinate and manage crises. Crises amplify existing inequalities, hitting the most vulnerable the hardest — and this can erode trust across society at exactly the time when trust is most needed. New technologies and forms of communication offer ways for citizens to be engaged and empowered. Involving civil society can bring many advantages, producing a better informed, tailored, and localised response.@en

Android malicious applications have become so sophisticated that they can bypass endpoint protection measures. Therefore, it is safe to admit that traditional anti-malware techniques have become cumbersome, thereby raising the need to develop efficient ways to detect Android malware. In this paper, we present Hybroid, a hybrid Android malware detection and categorization solution that utilizes program code structures as static behavioral features and network traffic as dynamic behavioral features for detection (binary classification) and categorization (multi-label classification). For static analysis, we introduce a natural-language-processing-inspired technique based on function call graph embeddings and design a graph-neural-network-based approach to convert the whole graph structure of an Android app to a vector. For dynamic analysis, we extract network flow features from the raw network traffic by capturing each application’s network flow. Finally, Hybroid utilizes the network flow features combined with the graphs’ vectors to detect and categorize the malware. Our solution demonstrates 97.0% accuracy on average for malware detection and 94.0% accuracy for malware categorization. Also, we report remarkable results in different performance metrics such as F1-score, precision, recall, and AUC.

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Malicious software, widely known as malware, is one of the biggest threats to our interconnected society. Cybercriminals can utilize malware to carry out their nefarious tasks. To address this issue, analysts have developed systems that can prevent malware from successfully infecting a machine. Unfortunately, these systems come with two significant limitations. First, they frequently target one specific platform/architecture, and thus, they cannot be ubiquitous. Second, code obfuscation techniques used by malware authors can negatively influence their performance. In this paper, we design and implement HawkEye, a control-flow-graph-based cross-platform malware detection system, to tackle the problems mentioned above. In more detail, HawkEye utilizes a graph neural network to convert the control flow graphs of executable to vectors with the trainable instruction embedding and then uses a machine-learning-based classifier to create a malware detection system. We evaluate HawkEye by testing real samples on different platforms and operating systems, including Linux (x86, x64, and ARM-32), Windows (x86 and x64), and Android. The results outperform most of the existing works with an accuracy of 96.82% on Linux, 93.39% on Windows, and 99.6% on Android. To the best of our knowledge, HawkEye is the first approach to consider graph neural networks in the malware detection field, utilizing natural language processing.

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Android is the most dominant operating system in the mobile ecosystem. As expected, this trend did not go unnoticed by miscreants, and quickly enough, it became their favorite platform for discovering new victims through malicious apps. These apps have become so sophisticated that they can bypass anti-malware measures implemented to protect the users. Therefore, it is safe to admit that traditional anti-malware techniques have become cumbersome, sparking the urge to come up with an efficient way to detect Android malware. In this paper, we present a novel Natural Language Processing (NLP) inspired Android malware detection and categorization technique based on Function Call Graph Embedding. We design a graph neural network (graph embedding) based approach to convert the whole graph structure of an Android app to a vector. We then utilize the graphs' vectors to detect and categorize the malware families. Our results reveal that graph embedding yields better results as we get 99.6% accuracy on average for the malware detection and 98.7% accuracy for the malware categorization.

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Android is the most popular smartphone operating system. At the same time, miscreants have already created malicious apps to find new victims and infect them. Unfortunately, existing anti-malware procedures have become obsolete, and thus novel Android malware techniques are in high demand. In this paper, we present Falcon, an Android malware detection and categorization framework. More specifically, we treat the network traffic classification task as a 2D image sequence classification and handle each network packet as a 2D image. Furthermore, we use a bidirectional LSTM network to process the converted 2D images to obtain the network vectors. We then utilize those converted vectors to detect and categorize the malware. Our results reveal that Falcon could be an accurate and viable solution as we get 97.16% accuracy on average for the malware detection and 88.32% accuracy for the malware categorization.

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In order to ensure successful product development processes, manifold modelling approaches have been developed, which cover a wide range of aspects such as responsibilities, duration of activities and dependencies. Still, an industry standard does not exist. Users of process modelling approaches are driven by different targets depending on the respective role. Currently, practitioners need to evaluate strengths and weaknesses of each approach by themselves and find little guidance for the selection. As a consequence, users might select unsuitable approaches and do not get the expected result. Thus, the intended applications of the model such as analyses or an optimization of the process are hampered. This could heavily affect companies´ success by product or project failures. The paper shows the concept of a recommendation tool that enables a suitable and effective selection of process modelling approaches. Key element is the description of relevant use cases and personas that represent the various needs of both different company types as well as different roles within such as process modellers and users. By identifying the most relevant case, each practitioner will be successfully guided to the most suitable modelling approach.@en

Models of products and design processes are key to interacting with engineering designs and managing the processes by which they are developed. In practice, companies maintain networks of many interrelated models which need to be synthesised in the minds of their users when considering issues that cut across them. This article considers how information from product and design process models can be integrated with a view to help manage these complex interrelationships. A framework highlighting key issues surrounding model integration is introduced and terminology for describing these issues is developed. To illustrate the framework and terminology, selected modelling approaches that integrate product and process information are discussed and organised according to their levels and forms of integration. Opportunities for further work to advance integrated modelling in engineering design research and practice are discussed.

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In many engineering design contexts models are indispensable. They offer decision support and help tackle complex and interconnected design projects, capturing the underlying structure of development processes or resulting products. Because managers and engineers base many decisions on models, it is crucial to understand their properties and how these might influence their behaviour. The level of detail, or granularity, of a model is a key attribute that results from how reality is abstracted in the modelling process. Despite the direct impact granularity has on the use of a model, the general topic has so far only received limited attention and is therefore not well understood or documented. This article provides background on model theory, explores relevant terminology from a range of fields and discusses the implications for engineering design. Based on this, a classification framework is synthesised, which outlines the main manifestations of model granularity. This research contributes to theory by scrutinising the nature of model granularity. It also illustrates how this may manifest in engineering design models, using Design Structure Matrices as an example, and discusses associated challenges to provide a resource for modellers navigating decisions regarding granularity.

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The design of many products is incremental, based on a prior architecture and may be thought of as a series of changes to an existing design. Nonetheless, design changes and their propagation complicate design process planning. They are a major source of rework and lead to frequent rescheduling and reprioritisation of design tasks. Few methods exist to estimate the impact of such planning decisions with regard to both the design process and the product. Process simulation may provide support in this context when it is based on an appropriate description of the product. However, it can be hard to determine a suitable level of description, or granularity, for the product model. This article explores, by reference to a diesel engine, the implications of product model granularity for simulating design change propagation, and design process planning.

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