Publication Type : Conference Paper
Publisher : Springer
Source : Advances in Signal Processing, Embedded Systems and IoT- Proceedings of Seventh ICMEET- 2022, 2022.
Url : https://link.springer.com/chapter/10.1007/978-981-19-8865-3_30
Campus : Bengaluru
School : School of Computing
Verified : Yes
Year : 2022
Abstract : One of the most pressing concerns in the Internet of Things (IoT) is security. The majority of IoT deployments rely on the creation of a wireless sensor network (WSN), which builds Low Power and Lossy Networks (LLNs) between a large number of constrained devices. WSN is an IPv6 low-power personal area network (6LoWPAN) communication protocol that uses the routing protocol for Low Power and Lossy Networks (RPL) for routing. Providing security to RPL network devices is difficult due to the resource constraints of the devices attached to RPL /IPv6. This work introduces attack detection using SVM (ADSVM) protocol to detect attacks before they have a significant impact on the IoT. Different WSNs are created considering the mobility factor of nodes. To forecast the attack, a new dataset centered on IoT traits is created and analyzed. Using eightfold cross-validation for attack prediction, the dataset has an accuracy of 84.37%.
Cite this Research Publication : Panda, N. and Supriya, M., "Blackhole Attack Prediction in Wireless Sensor Networks using Support Vector Machine," Advances in Signal Processing, Embedded Systems and IoT- Proceedings of Seventh ICMEET- 2022, 2022. (Presented)