Detecting selective forwarding attacks in wireless sensor networks using support vector machines
AuthorKaplantzis, Sophia; SHILTON, ALISTAIR; Mani, Nallasamy; Sekercioglu, Ahmet
Source TitleProceedings, 3rd International Symposium on Intelligent Sensors, Sensor Networks and Information Processing
PublisherInstitute of Electrical and Electronic Engineers
University of Melbourne Author/sSHILTON, ALISTAIR
AffiliationEngineering: Department of Electrical and Electronic Engineering
Document TypeConference Paper
CitationsKaplantzis, S., Shilton, A., Mani, N., & Sekercioglu, A. (2007). Detecting selective forwarding attacks in wireless sensor networks using support vector machines. In Proceedings, 3rd International Symposium on Intelligent Sensors, Sensor Networks and Information Processing, Melbourne.
Access StatusOpen Access
Wireless Sensor Networks (WSNs) are a new technology foreseen to be used increasingly in the near future due to their data acquisition and data processing abilities. Security for WSNs is an area that needs to be considered in order to protect the functionality of these networks, the data they convey and the location of their members. The security models and protocols used in wired and other networks are not suited to WSNs because of their severe resource constraints, especially concerning energy . In this article, we propose a centralized intrusion detection scheme based on Support Vector Machines (SVMs) and sliding windows. We find that our system can detect black hole attacks and selective forwarding attacks with high accuracy without depleting the nodes of their energy.
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