Work place: College of Software, Sungkyunkwan University, Suwon, 16419, Republic of Korea
E-mail: thcho@skku.edu
Website:
Research Interests: Computer systems and computational processes, Computer Architecture and Organization, Systems Architecture, Solid Modeling
Biography
Taeho Cho received his Ph.D. in Electrical and Computer Engineering from the University of Arizona, USA, in 1993, and B.S. and M.S. degrees in Electrical Engineering from Sungkyunkwan University, Korea, and the University of Alabama, USA, respectively. He is currently a Professor in the College of Software at Sungkyunkwan University, Korea. His research interests include wireless sensor networks, intelligent systems, modeling and simulation, and enterprise resource planning.
DOI: https://doi.org/10.5815/ijitcs.2017.04.01, Pub. Date: 8 Apr. 2017
A wireless sensor network's sensor nodes have scarce resources, are exposed to the open environment, and use wireless communication. These features make the network vulnerable to physical capture and security attacks, therefore adversaries attempt various attacks such as false report injection attacks. A false report injection attack generates a false alarm by forwarding a false report to the base station. It confuses a user and lowers the reliability of the system. In addition, it leads to depletion of the node energy in the process of delivering a false report. A dynamic en-route filtering scheme performs detection in the data transfer process, but it incurs unnecessary energy loss in a continuous attack situation. In this paper, in order to solve this problem, a scheme is proposed for determining whether or not to redistribute keys at execution. The proposed scheme saves energy by detecting false reports at an earlier hop than the existing scheme by using fuzzy logic and the feature of a loaded secret key of each node in the key pre-distribution phase. Furthermore, it improves the detection performance with an appropriate re-distribution of the key. Experimental results show up to 52.33% energy savings and an improved detection performance of up to 18.57% compared to the existing scheme.
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