The growth of the Internet of Things (IoT) has led to the increase of new threats, particularly IoT botnets that are a serious cybersecurity concern. This paper proposes a Machine Learning (ML) framework using black-box models for IoT botnet detection. It offers a trade-off between performance - high model accuracy - and interpretability - by providing security experts with explainable results. Our experimentation on real traffic data infected with Mirai and Bashlite malwares, showed that the framework achieves the best accuracy using random forest and extra tree models. In addition, it provides security experts with information on features that are important for a particular instance (i.e., local interpretation) and for the whole dataset (i.e., global interpretation), allowing them to trust the models results and save time and resources.

Systems-on-a-chip (SoC) represents a rupture on the traditional HPC infrastructure and started to be adopted together OS-level virtualization to provide services in Fog and Edge computing scenarios. In this work, we analyzed the performance of OS-level virtualization solutions - Linux Containers (LXC) and Docker - for HPC activities running in SoC systems in order to discover how OS-level virtualization and resource sharing affects the performance of virtualized environments. We evaluated CPU and (network and inter-process) communication performance taking in account two different scenarios: without communication between benchmark processes in execution and; with processes communication in a cooperative way. Results shows that both tools are suitable for HPC applications in SoC systems, despite the notice of some performance reduction due resource sharing.

Organizations need process data in real time. Cloud Computing is a common choice but it presents high latency on data transfer. As an alternative to reduces the latency, Edge computing based on System-on-a-Chip systems (SoC-based systems) can retain part of the data processing, allowing resource sharing among multiple requests through the use of OS-level virtualization. In this work, we analyze the performance of two popular OS-level virtualization solutions - Linux Containers (LXC) and Docker - for I/O-bound activities running in SoC systems, in order to understand how resource sharing affects the processing performance.

Pervasive Information System (PIS) represents a new generation of Information Systems (IS) available anytime, anywhere in a pervasive environment. In this paper, we propose to enhance PIS transparency and efficiency through a context-aware intentional service prediction approach. This approach allows anticipating user's future needs, offering and recommending him the most suitable service in a transparent and discrete way. We detail in this paper our service prediction mechanism and present encouraging experimental results demonstrating our proposition.

Information Systems (IS) have massively adopted service orientation by exposing their functionalities as services. With the evolution of mobile technologies (smartphones, 3G/4G networks, etc.), such systems are now confronted with a new pervasive environment for which they were not originally designed. Indeed, pervasive environments are characterized by their heterogeneity and dynamicity due to their evolving context and their need for transparency. None of these features are particularly considered in traditional IS designed for stable and controlled office environments. In our new vision for service-oriented Pervasive Information Systems (PIS), the user becomes the center of these systems. This paper presents a user-centric service-oriented vision for PIS based on a context-aware intentional approach, which considers the user intention and the context in which this intention arises as a guiding principle for service description, discovery, prediction and recommendation.

Service Discovery Protocols have been developed for applications to take advantages of services shared over networks. Each protocol provides a way for applications to ask for the type of services they need. In return, applications receive information about services that could fulfill their needs. However, such protocols do not supply enough support for dynamic environments involving interaction among persons, heterogenous devices, applications and files. Unlike applications, people expect to receive customized information about their identity, role, social relations, and even contextual variables (e.g., location). Thus, to provide a computational support that satisfies users' requirements in an accurate way, up to date information about the current conditions of the environment and the entities involved in it is fundamental. In this paper, we tackle the lack of a descriptive technique for resource discovery by proposing a carefully designed ontological approach.

Pervasive Information System (PIS) provides a new vision of Information System available anytime and anywhere. The users of these systems must evolve in a space of services, in which several services are offered to him. However, PIS should enhance the transparency and efficiency of the system. We believe that a user-centric vision is needed to ensure a transparent access to the frequently changing space of services regardless of how to perform it. In this paper, we propose a new approach of PIS, both context-aware and intentional called IPIS. In this approach, services are proposed in order to satisfy user's intention in a given context. Then, we propose a context-aware intentional service discovery mechanism. Such mechanism is based on an extension of OWL-S taking into account the notion of context and intention. We present in this paper IPIS platform. Then, we detail the proposed service discovery mechanism and present experimental results that demonstrate the advantage of using our proposition.

Despite its growing acceptance, Service-Oriented Computing (SOC) remains a computing mechanism to speed up the design of software applications by assembling ready-made software services. We argue that it is difficult for business people to fully benefit of SOC if it remains at the software level. The paper proposes a move toward a description of services in business terms, i.e., intentions and strategies to achieve them and to organize their publication, search, and composition on the basis of these descriptions. In this way, it leverages SOC to an intentional level, ISOC. We present ISM, the model to describe intentional services, and populate the service registry with their descriptions. We highlight its intention-driven perspective for service description, retrieval, and composition. Thereafter, we propose a methodology to determine intentional services that meet business goals and to publish them in the registry. Finally, the paper introduces a set of transformations to bridge the gap from the intentional level to the implementation one.

Services based applications are considered as support for both Business-to-Consumer interactions and Business-to-Business collaborations. However, Service Oriented Computing (SOC) remains at the software level and business people cannot exploit fully its benefits. In this context, the Intentional Service Oriented Architecture (ISOA) suggests a move from the function-driven Service Oriented Computing (SOC) to intention-driven SOC in order to provide service description understandable by business practitioners. Intentional services represented business oriented services in an intentional perspective, i.e. focusing on the goal it allows achieving rather on the functionality it performs. However, current intentional service omits the quality of service (QoS) dimension. In this paper, we focus on the quality elicitation process to apply for (1) capturing customers' Quality requirements and (2) describing the quality of service on an intentional service specification. We consider that the elicitation of the quality of a service during the requirements engineering (RE) phase can contribute to a customer-centered adaptability of service composition by selecting the service composition that will satisfy customers' quality requirements. The aim is to provide a goal driven approach to reason on quality for both documenting intentional service and discovering the quality requirements of business customers.

Recent advances in communication technology enable the emergence of a new generation of applications that integrates mobile devices with classical high performance systems as part of a common computing environment. In such environments, keeping the coherence of shared data (distributed objects, for example) represents a real challenge as communications are strongly influenced by the performance and the reliability of mobile devices (laptops, PDAs and cellular telephones) and wireless networks (WiFi, Bluetooth). Indeed, data incoherence may arise due to message losses or node volatility, which blocks the algorithms used to synchronize these data. In this paper, we analyze the main challenges concerning the manipulation of shared distributed objects in a pervasive environment. We demonstrate how a membership service can be enhanced to tolerate temporary disconnections and message losses without blocking, while reducing the number of exchanged message.

In this paper, we present a framework for context-based awareness support designed for Web-based groupware systems. Nowadays, such systems can be accessed through Web-enabled mobile devices, such as PDAs and cellular phones, which allow their users to perform simple tasks, such as contacting colleagues or reading messages. However, these devices present some constraints related to their intrinsic characteristics (reduced display size, limited memory capacity). These constraints make more acute the need for information filtering. We present a framework for awareness support which is able to adapt the awareness information to the user's context, in order to reduce the risk of information overload. This framework, called BW-M, selects among the available awareness information that which matches the user's, represented through pre-defined profiles, according to her/his current context.

This paper introduces a framework for awareness support in groupware systems. This framework, called Big Watch (BW), intends to support past event awareness. It has been designed to be used when developing new groupware applications and also to improve existent ones. This paper summarizes the characteristics and the structure of BW, and discusses two applications that use it.