  The number of connected smart devices has been increasing exponentially for
different Internet-of-Things (IoT) applications. Security has been a long run
challenge in the IoT systems which has many attack vectors, security flaws and
vulnerabilities. Securing billions of B connected devices in IoT is a must task
to realize the full potential of IoT applications. Recently, researchers have
proposed many security solutions for IoT. Machine learning has been proposed as
one of the emerging solutions for IoT security and Reinforcement learning is
gaining more popularity for securing IoT systems. Reinforcement learning,
unlike other machine learning techniques, can learn the environment by having
minimum information about the parameters to be learned. It solves the
optimization problem by interacting with the environment adapting the
parameters on the fly. In this paper, we present an comprehensive survey of
different types of cyber-attacks against different IoT systems and then we
present reinforcement learning and deep reinforcement learning based security
solutions to combat those different types of attacks in different IoT systems.
Furthermore, we present the Reinforcement learning for securing CPS systems
(i.e., IoT with feedback and control) such as smart grid and smart
transportation system. The recent important attacks and countermeasures using
reinforcement learning B in IoT are also summarized in the form of tables. With
this paper, readers can have a more thorough understanding of IoT security
attacks and countermeasures using Reinforcement Learning, as well as research
trends in this area.

Machine Learning;Artificial Intelligence