@ARTICLE{10.21494/ISTE.OP.2019.0324, 

TITLE={Reinforcement Learning and Blockchain to secure the Internet of Things}, 
  
AUTHOR={Aissam Outchakoucht, Hamza Es-Samaali, Anas Abou El Kalam, Siham Benhadou, }, 
	
JOURNAL={Internet of Things}, 
	
VOLUME={3}, 

NUMBER={Issue 1}, 
	
YEAR={2019}, 

URL={http://www.openscience.fr/Reinforcement-Learning-and-Blockchain-to-secure-the-Internet-of-Things}, 
	
DOI={10.21494/ISTE.OP.2019.0324}, 
	
ISSN={2514-8273}, 
	
ABSTRACT={Securing the IoT world in not a luxury task; it is even a matter of urgency given this exponential growth of IoT market. In fact, one can easily imagine the catastrophic damages of an attack in the field of e-Health or in the smart cities and critical infrastructures management. That being said, serious problems derived from these constrained environments block the proposal of pertinent solutions. This paper is a contribution step in this direction. To address these problems, we expose a global framework inspired from the concept of emergence in order to take advantage of this large number of devices and extract the "emergent" characteristics that are nonexistent in smaller systems. The framework is built on top of two pillars: Blockchain as architecture and Reinforcement Learning as processing toolkit.}}