The growing Internet of Things (IoT) market introduces new security challenges for network administrators. Most IoT devices are poorly configured making them a target of choice for attackers. Mirai botnet illustrates the threat posed by IoT devices. In this context, Machine Learning techniques can be leveraged to detect attacks in IoT networks. Indeed, contrary to desktop computers or laptops, IoT devices are used for very specific tasks. Therefore, the generated network traffic follows a predictable pattern making data analysis techniques well suited to detect a deviation from the expected behavior. In this paper, we present machine learning based techniques for IoT network monitoring. We first built an experimental smart home network to generate network traffic data. The network traffic is described using features, such as the size of the first N packets sent and received along with the corresponding inter-arrival times. We then train and test classification algorithms for devices recognition purposes. We also describe how to use autoencoders for anomaly detection in IoT networks.