Abstract
The network of smart devices and gadgets forms the Internet of things (IoT). The IoT technology implemented in our day-to-day devices has shown more advantages to the users. With this, the use of IoT devices has also increased which increases the network traffic. An increase in network traffic has attracted many hackers to inject more network attacks. The more the usage, the more it is vulnerable to attacks. One such IoT attack is the RPL protocol wormhole attack. Thus, there is a need for an intrusion detection system (IDS) to protect the network data. The proposed work concentrates on generating real-time wormhole attacks in the Cooja simulator, and using a recurrent neural network (RNN), deep learning model to detect and classify the wormhole attack data from the normal data in the IoT network traffic. The proposed work produced an accuracy of 96%. The F1 score produced is 96%.
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Thiyagu, T., Krishnaveni, S., Arthi, R. (2022). Deep Learning Approach for RPL Wormhole Attack. In: Hemanth, D.J., Pelusi, D., Vuppalapati, C. (eds) Intelligent Data Communication Technologies and Internet of Things. Lecture Notes on Data Engineering and Communications Technologies, vol 101. Springer, Singapore. https://doi.org/10.1007/978-981-16-7610-9_23
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DOI: https://doi.org/10.1007/978-981-16-7610-9_23
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