Neural network based approach for malicious node detection in wireless sensor networks
Curiac, Daniel, Volosencu, Constantin, Doboli, Alex, Dranga, Octavian, and Bednarz, Tomasz (2007) Neural network based approach for malicious node detection in wireless sensor networks. In: Proceedings of the WSEAS International Conference on Circuits, Systems, Signal and Telecommunications, pp. 8-13. From: 2007 WSEAS International Conference on Circuits, Systems, Signal and Telecommunications, 17-19 January 2007, Gold Coast, QLD, Australia..
PDF (Published version)
Restricted to Repository staff only
Even if their resources in tenus of energy, memory, computational power and bandwidth are strictly limited, sensor networks have proved their huge viability in the real world, being just a matter of time until this kind of networks will be standardized and used broadly in the field. One of the important problems that are related to the use of wireless sensor networks in harsh environments is the gap in their security. This paper provides a solution to discover malicious nodes in wireless sensor networks using an on-line neural network predictor based on past and present values obtained from neighboring nodes. This solution can be also a way to discover the malfunctioning nodes that were not a subject of an attack. Being localized on the base station level, our algorithm is suitable even for large-scale sensor networks.
|Item Type:||Conference Item (Refereed Research Paper - E1)|
|Keywords:||wireless sensor network; analytical redundancy; prediction; malicious node; neural network|
|Date Deposited:||15 Dec 2009 02:56|
|FoR Codes:||10 TECHNOLOGY > 1005 Communications Technologies > 100599 Communications Technologies not elsewhere classified @ 50%
09 ENGINEERING > 0906 Electrical and Electronic Engineering > 090699 Electrical and Electronic Engineering not elsewhere classified @ 50%
|SEO Codes:||89 INFORMATION AND COMMUNICATION SERVICES > 8999 Other Information and Communication Services > 899999 Information and Communication Services not elsewhere classified @ 100%|