Projet de thèse en Informatique
Thèses en préparation à Paris Saclay , dans le cadre de Sciences et Technologies de l'Information et de la Communication , en partenariat avec Télécom SudParis (France) (laboratoire) , R3S (equipe de recherche) et de Télécom SudParis (établissement de préparation de la thèse) depuis le 15-11-2016 .
GECO-IoT, IoT Trust Management For Smart Factories
By the year 2020 it is expected that the number of connected objects will exceed several billions devices. These objects will be present in everyday life for a smarter home and city as well as in future smart factories that will revolutionize the industry organization. In this future world machines will talk to machines (M2M) to organize the production and coordinate their actions function of the information collected by different sensors and exchanged with other entities. Thanks to the Internet of Things, Smart Factories will enable more intelligent monitoring and self-organizing capabilities than traditional factories. As a consequence, the production process will be more efficient and flexible with products of higher quality. Digital data exchange between machines and sensors is expected to occur beyond the traditional physical limit of a simple factory plant. Several machines and sensors deployed on different sites will use the Internet to communicate together in order to control a wide value chain. Without any doubt the use of Internet to enable communication between machines located in different factories offers new opportunities to the smart industries but also creates new challenges. In fact, opening connectivity to the external world raises questions about data and IT infrastructure security that were not an issue when most of the machines were controlled locally and only few of them connected to some other remote system. Special care must be taken to make the transition as smooth as possible and more importantly take the necessary measurements to protect internal information and knowledge from being stolen and from malicious cyber-attackers that may harm the production processes and put the machines out of order. Precedent cyber attacks such as the one against Ukraine Power industry, or the one against the Norway oil and gas industry, and the permanent threats such as Stuxnet, Dragonfly and Black energy, give a clear idea about the importance of the harm and what the future Smart Industry have to face and prepare itself to protect its assets. Such threats may become very quickly a national problem and may even influence the country stability if they affect for instance the nuclear energy plants, oil and gas sector, water distribution, . The need for excellent cybersecurity on industrial control systems has never been important  , or more urgent than nowadays. These systems enable smart factories control their field devices, collect data and detect problems. And they are more and more under serious threats. Many recent initiatives are being launched by many universities all over the world to deal with IoT security like the Stanford Secure Internet of Things Project which is a cross-disciplinary research effort between computer science and electrical engineering faculty at Stanford University, UC Berkeley, and the University of Michigan . Some other are being supported by national governments like the PETRAS consortium of nine leading UK universities that will work together over the next three years to explore critical issues in privacy, ethics, trust, reliability, acceptability, and security. Lancaster University, a member of this hub will concentrate on a number of key areas, including the security considerations for connected devices in critical infrastructure and industrial environments . Smart Factories will be composed of several complex communicating systems combining wired and wireless systems with different devices sizes and computing capacities. The variety of systems implies various scenarios of attacks and eavesdropping. Cyber espionage and cyber sabotage are the most significant threats to smart factories. Cyber security systems must offer adapted mechanisms to cope with these challenges and provide the Availability, Integrity and Confidentiality of the systems. As stated earlier, the use of Internet of Things (IoT) systems is expected to spread in modern factories. IoT may be seen as a potential security hole where attackers may use to penetrate into the factory system for eavesdropping or damaging purposes. Generally speaking building a cyber secure system does not only mean applying a traditional cryptographic system to protect the exchange of data flows. But it requires also building a system where the data itself is being trusted by the different stakeholders. When it comes to the IoT additional issues are raised due to its specific properties. Data trust collection is an important and delicate task. In fact, if the data collected by the physical sensors is not trustworthy (a situation due to a physical problem or malicious intruder for example), then the rest of the upper layers may get impacted, and the collected data may be useless. Moreover, the processing of the collected data should be also trusted. In IoT, the data collected usually goes through different intermediate steps of data fusion and analysis before handing possibly only a subset of it to a sink or to the cloud. These steps must preserve a high level of security and privacy, and offer a reliable data processing that avoids any loss of an important precision. This sensitive phase must be accomplished carefully. At the same time if we want to take profit from advanced IoT services, we need to collect accurate information that will allow a fine grained analysis. This means that we may need to disclose some information like the identity of the sensors and the context of use while requiring privacy. These opposite requirements are not an easy task to fulfill. Trust is an important issue in the IoT in general and it has a great importance for the IoT used in Smart Factories. However, several challenges are still open: The concept of trust has not yet a clear meaning in IoT [1,2]. We have firstly to focus on proposing a clear definition of the trust concept based on the IoT requirements. This includes the definition of its parameters, their evaluations and the related algorithms. Indeed, several trust solutions exist in the literature[1,2,3]. However, each one of them evaluates a set of parameters that are needed applied to the addressed environment: some solutions are based on the exchange of attributes , others are based on combined parameters in P2P networks . On the other hand, some existing approaches are based on the feedback of the different entities in the network , etc. As a result, a fundamental issue is still open, which is, the definition of the trust parameters for IoT and their evaluations. Reputation: An IoT trust framework should define a trust reputation mechanism to share the trust feedbacks between objects, readers and portals. Several issues are related to its evaluation: 1. How to avoid the false feedback problem? 2. How to combine the different values? During the collection of feedback, we may receive different values, and sometimes they are contradictory. For example: the same object may be seen as a trusted entity in a Factory A and a malicious node with another Factory B. Therefore, we have to find a solution in order to combine them and to determine only one trust level. 3. How to share this value?, how to understand the received feedback? and how to confirm that the received value has the same meaning between the receiver and the sender? To solve these problems, some solutions define new communication protocols and others propose to use an ontology approach to solve it. For IoT, we have to compare between the existing solutions in the literature and to update the adequate ones to the IoT environment. Securing the communication between the devices is another important component of the whole security framework. In a typical IoT architecture, devices and systems are connected through heterogeneous networks employing various standard and proprietary protocols that should offer secure communication capabilities. Depending on the considered communication model: device-to-device, device-to-gateway, or device-to-cloud, several security solutions have been proposed in similar but yet different environments [11-12]. These studies proposed lightweight security mechanisms which are aware of the capacity and energy restrictions of the communicating devices. In this work, we will assume that the trust mechanisms rely on secure communications. But even under such assumption, the trust parameters will be highly impacted by the security mechanisms characteristics such as the integrity of the data exchanged. Hence, another scope of this thesis is to study the interaction between the trust model and the underlying security mechanisms when assuming different communication models that can be deployed in the smart factories environment. The Phd candidate will start by proposing a new trust model adapted to the IoT requirement having in mind the smart factories as a potential use case. By Trust model, we mean a generic model to give a clear definition of all the entities involved and their relationships in the trust chain. Then, he/she will focus mainly on two main trust parameters: by resolving the issues related to the reputation parameter (discussed before) and its evaluation, proposing and adapting a trust identity management for IoT. The proposed model must be implemented in a real IoT platform.