The large number of sensors and actuators that make up the Internet of Things obliges these systems to use diverse technologies and protocols. This means that IoT networks are more heterogeneous than traditional networks. This gives rise to new challenges in cybersecurity to protect these systems and devices which are characterized by being connected continuously to the Internet. Intrusion detection systems (IDS) are used to protect IoT systems from the various anomalies and attacks at the network level. Intrusion Detection Systems (IDS) can be improved through machine learning techniques. Our work focuses on creating classification models that can feed an IDS using a dataset containing frames under attacks of an IoT system that uses the MQTT protocol. We have addressed two types of method for classifying the attacks, ensemble methods and deep learning models, more specifically recurrent networks with very satisfactory results.

翻译：构成物联网的大量传感器和执行器使得这些系统必须采用多样化的技术与协议，导致物联网网络比传统网络更具异构性。这在网络安全领域引发新挑战——需保护这些持续连接互联网的系统与设备。入侵检测系统（IDS）被用于保护物联网系统免受网络层面的各种异常与攻击，而机器学习技术可提升入侵检测系统的性能。本研究聚焦于创建能供给入侵检测系统的分类模型，利用包含MQTT协议物联网系统攻击数据帧的数据集。我们采用了两种攻击分类方法：集成方法和深度学习模型（具体采用循环神经网络），并取得了非常理想的效果。