Securing IoT Networks: RPL Attack Detection with Deep Learning GRU Networks

Securing IoT Networks: RPL Attack Detection with Deep Learning GRU Networks

  IJRES-book-cover  International Journal of Recent Engineering Science (IJRES)          
  
© 2023 by IJRES Journal
Volume-10 Issue-2
Year of Publication : 2023
Authors : Raveendranadh Bokka, Tamilselvan Sadasivam
DOI : 10.14445/23497157/IJRES-V10I2P103

How to Cite?

Raveendranadh Bokka, Tamilselvan Sadasivam, "Securing IoT Networks: RPL Attack Detection with Deep Learning GRU Networks," International Journal of Recent Engineering Science, vol. 10, no. 2, pp. 13-21, 2023. Crossref, https://doi.org/10.14445/23497157/IJRES-V10I2P103

Abstract
The Internet of Things (IoT) can be defined as the internet-based connectivity of heterogeneous intelligent devices to control and run them. Smart gadgets and wireless networks are vulnerable to numerous routing attacks due to their open nature, worldwide connection, and resource constraints. The Routing Protocol for Low-Power Lossy Networks (RPL) is a prominent routing protocol used in IoT-based networks to design routing paths for resource-constrained devices. However, RPL's built-in security features do not prevent most routing attacks. Because IoT devices generate a vast quantity of data, we presented a Deep Learning-based GRU network in this study for detecting threats in RPL-based IoT networks. Our proposed data set contains traffic traces for normal scenarios and attack scenarios such as Sinkhole, Blackhole, Sybil, Selective Forwarding, DIS flooding, and DIO suppression with 21 features for 20 static nodes generated using the NetSim Standard version 12.1 software tool. The GRU model was trained and tested with 80% and 20% of the dataset. Metrics, including accuracy, precision, recall, f1-score, and AUC, are used to evaluate the model's performance. The model attained a testing accuracy of 95.51 percent, precision, recall, and f1-score values of 0.94, 0.81, and 0.87 for an attack class and 0.96, 0.99, and 0.97 for a normal class, respectively. The model's AUC value is 0.899, indicating that our suggested model can differentiate the attack and normal classes by almost 90%.

Keywords
Internet of Things (IoT), 6LoWPAN, RPL, Security, Attacks Detection, Deep Learning, GRU, NetSim.

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