Machine Learning could be used for improving the IoT systems.
For example, you could make the Edge device smarter by using ML models to predict the next action.
Also, Amazon Alexa and Google Assistant are using Deep Learning models to understand the user's voice.
Or, you could use some sort of Anomaly Detection to detect the anomalies in the IoT data, or train classification models for threat detection.
The BoT-IoT dataset was created by designing a realistic network environment in the Cyber Range Lab of UNSW Canberra. The network environment incorporated a combination of normal and botnet traffic. The dataset’s source files are provided in different formats, including the original pcap files, the generated argus files and csv files. The files were separated, based on attack category and subcategory, to better assist in labeling process.
You could find the full dataset here.
Also, you could find the jupyter notebooks here.