With the recent progress in development of low-budget sensors and machine-to-machine communication, the Internet of Things (IoT) has attracted considerable attention. Unfortunately, many of today's IoT devices are rushed to market with little consideration for basic security and privacy protection making them easy targets for various attacks. As a result, number of malware and their variants designed for IoT devices has been constantly increasing. Traditional intrusion detection approaches are unsuitable for IoT networks due to limited computational capacity of smart devices and diversity in their technology. In this paper, we propose a defense system for IoT networks based on software-defined networking and network function virtualization. The defense system core component is a reinforcement machine learning agent that evaluates risks of potential attack and takes the most optimal action in order to mitigate it.