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A Novel Realistic Dataset for Intrusion Detection in IoT based on Machine Learning

The safe deployment of Internet of Things (IoT) devices has emerged as one of the most pressing issues in computer science. The Lossless Low Power Network (RPL) routing protocol is an excellent choice for Internet of Things routing. The ubiquity, connectivity, and low processing capabilities of IoT network devices identify them. These features, along with their exponential growth in recent years (more than 60 billion devices linked to the Internet by the end of 2021), have led in a surge in IoT-based cyber-attacks. To fight against these cyber-attacks, an intrusion detection system (IDS) is required to secure the privacy, availability, and performance of the IoT network. Unfortunately, most public data sets related to IDS, such as UNSW NB15 and KDD CUP99, are incompatible with the IoT network’s unique environment. To solve these issues, we created a data collection framework that includes the recording of network traffic from its unique environment to IoT device needs. The examined dataset’s advantages are tailored to 6LoWPAN/RPL and CoAP, the most widely used protocols for IoT network deployment.