IoT-RPL 2021: Cyber Attack Dataset Based on RPL Routing for IoT
Description
The Internet of Things (IoT) has emerged as a central focus within computer science research, with the Routing Protocol for Low Power and Lossy Networks (RPL) serving as a pivotal standard for IoT routing. Devices within IoT networks are characterized by their extensive connectivity, pervasive presence, and constrained processing capabilities. Identifying routing attacks on 6LoWPAN-based IoT devices poses a significant challenge due to network intricacies. Various techniques, including anomaly detection, have been proposed to detect and classify such attacks by analyzing network traffic characteristics. This study specifically addresses routing attacks targeting the widely utilized RPL protocol in 6LoWPAN-based IoT systems. The dataset (IoT-RPL) comprises ".csv" files documenting four distinct routing attacks—Blackhole Attack, Flooding Attack, DODAG Version Number Attack, and Decreased Rank Attack—obtained from the Cooja simulator. This dataset facilitates the development of Intrusion Detection Systems (IDS) for RPL-based IoT networks using Artificial Intelligence and Machine Learning methods, eliminating the need for attack simulations. Simulating these attacks under realistic conditions is crucial for testing protection mechanisms. This study offers an alternative to traditional intrusion detection systems, emphasizing the importance of identifying relevant attack attributes, analyzing network traffic data, and ensuring dataset balance and representativeness. In conclusion, this research represents a significant advancement in IoT security, providing a new dataset to support the application of artificial intelligence and machine learning methods in detecting routing attacks on IoT devices.