Autonomic computing architecture for SCADA cyber security

Cognitive computing relates to intelligent computing platforms that are based on the disciplines of artificial intelligence, machine learning, and other innovative technologies. These technologies can be used to design systems that mimic the human brain to learn about their environment and can autonomously predict an impending anomalous situation. IBM first used the term ‘Autonomic Computing’ in 2001 to combat the looming complexity crisis (Ganek and Corbi, 2003). The concept has been inspired by the human biological autonomic system. An autonomic system is self-healing, self-regulating, self-optimising and self-protecting (Ganek and Corbi, 2003). Therefore, the system should be able to protect itself against both malicious attacks and unintended mistakes by the operator

A systematic literature review of cloud computing in eHealth

Cloud computing in eHealth is an emerging area for only few years. There needs to identify the state of the art and pinpoint challenges and possible directions for researchers and applications developers. Based on this need, we have conducted a systematic review of cloud computing in eHealth. We searched ACM Digital Library, IEEE Xplore, Inspec, ISI Web of Science and Springer as well as relevant open-access journals for relevant articles. A total of 237 studies were first searched, of which 44 papers met the Include Criteria. The studies identified three types of studied areas about cloud computing in eHealth, namely (1) cloud-based eHealth framework design (n=13); (2) applications of cloud computing (n=17); and (3) security or privacy control mechanisms of healthcare data in the cloud (n=14). Most of the studies in the review were about designs and concept-proof. Only very few studies have evaluated their research in the real world, which may indicate that the application of cloud computing in eHealth is still very immature. However, our presented review could pinpoint that a hybrid cloud platform with mixed access control and security protection mechanisms will be a main research area for developing citizen centred home-based healthcare applications

Qualité de service dans l'IOT : couche de brouillard

Abstract : The Internet of Things (IoT) can be defined as a combination of push and pull from the technological side and human side respectively. This push and pull effect results in more connectivity among objects and humans in the near surrounding environments [1]. With the growth in the field of IoT, in recent times, the risk of real time failures has increased as well. The failures are often detected by certain points of vulnerability in the system. Narrowing down to the root causes we get the point of failures and that leads to the required measures to overcome them. This creates the need for IoT systems to have a proper Quality of Service (QoS) architecture. Thus, QoS is becoming a crucial issue with the democratization of IoT. QoS is the description or measurement of the overall performance of a service, such as a telephony or computer network or a cloud computing service, particularly the performance seen by the users of the network. In this study, we propose the methods of enforcement of QoS in IoT platforms. We will highlight the challenges and recurrent issues faced by all IoT platforms which in turn inspired us to build a generic tool to overcome these challenges by enforcing the QoS in all the IoT platforms with an easy to use set up. The main focus of this study is to enable QoS features in the Fog layer of the IoT architecture. Existing platforms and systems enabling QoS features in the Fog layer are also highlighted. Finally, we validate our proposed model by implementing it on our AMI-LAB platform.L'Internet des objets (IdO) (Internet of Things en anglais), peut être défini comme une combinaison d’interactions entre les Humains et le monde technologique de l’Internet. De cet effet résulte une interconnexion entre les objets physiques et les appareils technologiques dans leur environnement proche. Ces dernières années le domaine de l'IdO s’est beaucoup développé, entrainant ainsi une augmentation du risque de défaillances en temps réel. Les défaillances sont souvent détectées par certains points de vulnérabilité dans le système. En se concentrant sur les causes profondes, le point de défaillance peut être détecter, ce qui conduit aux mesures à mettre en place pour surmonter les défaillances. Les systèmes IdO ont donc besoin d'avoir une architecture de Qualité de Service (QdS) adéquate. Ainsi, la QdS devient un enjeu crucial avec la démocratisation de l'IdO. La QdS est la description ou la mesure de la performance globale d'un service, tel qu'un réseau de téléphonie ou informatique, ou un service de cloud computing, en particulier la performance perçue par les utilisateurs du réseau. Dans cette étude, nous proposons les méthodes de mise en œuvre de la QdS dans les plateformes IdO. Nous mettrons en lumière les défis et les problèmes récurrents rencontrés par toutes les plateformes IdO, qui nous ont inspirés à construire un outil générique pour surmonter ces défis en imposant la QdS dans toutes les plateformes IdO avec une configuration facile à utiliser. L'objectif principal de cette étude est de permettre les fonctionnalités de QdS dans la couche Fog de l'architecture IdO. Les plateformes et systèmes existants permettant les fonctionnalités de QdS dans la couche Fog sont également mis en évidence. Enfin, nous soulignons la validation de notre modèle en le mettant en œuvre sur notre plateforme AMI-LAB