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International audience; Digital Twin (DT) is one of the pillars of modern information technologies that plays an important role on industry's digitalization. A DT is composed of a real physical object, a virtual abstraction of the object and a bidirectional data flow between the physical and virtual components. This paper presents a DT-based tool, called TaS, to easily test and simulate IoT environments. The objective is to improve the testing methodologies in IoT systems to evaluate the possible impact of it on the physical world. We provide the conditions to test, predict errors and stress application depending on hardware, software and real world physical process. The tool is based on the DT concept in order to detect and predict failures in evolving IoT environments. In particular, the way to prepare the DT to support fault injection and cybersecurity threats is analyzed. The TaS tool is tested through an industrial case study, the Intelligent Transport System (ITS) provided by the INDRA company. Results of experiments are presented that show that our DT is closely linked to the real world.

International audience; Requests to improve the quality of software are increasing due to the competition in software industry and the complexity of software development integrating multiple technology domains (e.g., IoT, Big Data, Cloud, Artificial Intelligence, Security Technologies). Measurements collection and analysis is key activity to assess software quality during its development live-cycle. To optimize this activity, our main idea is to periodically select relevant measures to be executed (among a set of possible measures) and automatize their analysis by using a dedicated tool. The proposed solution is integrated in a whole PaaS platform called MEASURE. The tools supporting this activity are Software Metric Suggester tool that recommends metrics of interest according several software development constraints and based on artificial intelligence and MINT tool that correlates collected measurements and provides near real-time recommendations to software development stakeholders (i.e. DevOps team, project manager, human resources manager etc.) to improve the quality of the development process. To illustrate the efficiency of both tools, we created different scenarios on which both approaches are applied. Results show that both tools are complementary and can be used to improve the software development process and thus the final software quality

International audience; This paper presents a study of existing threat detection techniques in cloud computing, together with an experimental evaluation of a subset of them. We consider the threats defined in the Cloud Security Alliance (CSA) report as well as the techniques for their detection, starting from classical signature-based approaches and finishing with recent machine learning based techniques. This paper also contains an analysis of original results presented in international conferences, published as journal papers, Internet resources, and standards. The main contributions of the study include: (1) providing a closer relationship between top threats in cloud computing and known detection techniques; (2) evaluating existing detection techniques concerning cloud computing principles and security challenges nowadays; and (3) reviewing commonly utilised datasets and their association with threats in the last five years. As existing detection techniques tend to target specific threats (or their groups), we also present the experimental evaluation of the applicability of known detection approaches against non-targeted threat groups

International audience; This paper presents a security assurance platform to monitor and control the security in the context of multi-cloud applications. Indeed, this property is a crucial issue in multi cloud-based environments where many aspects need to be faced, including risk management, data privacy and isolation, security-by-design applications, and vulnerability scans. Moreover, it also becomes necessary to have an efficient system that interrelates and operates all security controls that are configured and executed independently on each component of the system. In addition, as new attacks emerge every day, threat detection systems play a fundamental role in security monitoring schemes, identifying possible attacks. These systems handle an enormous volume of data, as they detect unknown malware by monitoring different activities from different points of observation, as well as adapting to new attack strategies and considering techniques to detect malicious behaviors and react accordingly. In this paper, we describe a monitoring platform for securing multi-cloud applications, from a Service Level Agreement perspective. Moreover, we present a case study depicting the multi-cloud monitoring of a smart-city transport application for the citizens of Tampere, Finland. Considering the nature of the application under study, the service requires continuous execution and availability functionalities, as end-users may utilize the service at any time