See the following paper: Agiollo, A., Conti, M., Kaliyar, P., Lin, T. N., & Pajola, L. (2021). "DETONAR: Detection of Routing Attacks in RPL-Based IoT", IEEE Transactions on Network and Service Management, 18(2), 1178–1190.

This paper illustrates the application of machine learning techniques to detect attacks in IoT networks.

The authors generated network traffic with a specific topology using NetSim and captured the traffic using the Wireshark sniffing tool. The simulated network consists of 17 IoT devices, including a border router (root node) in a DODAG structure. Each node transmits an application packet every second to the root node.

They developed an Intrusion Detection System (IDS) that uses a combination of signature and anomaly-based rules to identify malicious behavior in the traffic.

This setup led to the creation of the RADAR dataset, widely used for machine learning classification of attacks on IoT networks.

Perhaps this sources will be helpful to you.

the DARPA SPAR dataset, which is openly accessible and includes metadata and measurements of actual wireless channels.

. The UCI Machine Learning Repository: This repository has a number of datasets related to wireless

. The IEEE DataPort - this portal offers access to a multitude of datasets related to wireless communication, encompassing topics such as Internet of Things (IoT) applications, vehicle-to-everything (V2X) communications, and 5G networks.

Urbi Basu