The proliferation of IoT is evident in numerous domains today. Its applications are expanding rapidly, offering significant benefits. Nonetheless, it is plagued by numerous security flaws that can hinder its adoption in critical areas. This paper addresses the development of an intrusion detection system (IDS) for IoT networks. Specifically, we implement an autoencoder, an unsupervised deep learning model frequently employed in anomaly detection. The model is trained using the MQTTSet dataset, which contains both normal MQTT traffic and attack data. Training focused exclusively on legitimate data. Testing was conducted on both benign and malicious data to assess the model's effectiveness. The results indicate detection rates of 99.86% and 98.56% for normal and attack data, respectively.