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Erschienen in: Soft Computing 8/2019

03.12.2018 | Focus

An adaptive neuro-fuzzy logic based jamming detection system in WSN

verfasst von: K. P. Vijayakumar, K. Pradeep Mohan Kumar, K. Kottilingam, T. Karthick, P. Vijayakumar, P. Ganeshkumar

Erschienen in: Soft Computing | Ausgabe 8/2019

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Abstract

Wireless sensor network (WSN) is employed in variety of applications ranging from agriculture to military. WSN is vulnerable to various security attacks, in which jamming attacks obstruct and disturb the exchange of information between sensor nodes in WSN by transmitting signals to jam legitimate transmission to cause a denial of service. Hence, it is essential to secure the sensor networks from jamming attacks. In this paper, two approaches: fuzzy inference system (FIS) and adaptive neuro-fuzzy inference system (ANFIS)-based jamming detection system are proposed for detecting the presence of jamming by computing two jamming detection metrics, namely, packet delivery ratio and received signal strength indicator. FIS approach is based on Takagi–Sugeno fuzzy logic which optimizes the jamming detection metrics. ANFIS approach combines fuzzy logic and learning ability of the neural network to optimize the metrics for detecting various types of jamming. The proposed approaches are compared with existing system and themselves. The simulation result shows that the proposed ANFIS approach detects the jamming attacks as high as true detection ratio.

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Metadaten
Titel
An adaptive neuro-fuzzy logic based jamming detection system in WSN
verfasst von
K. P. Vijayakumar
K. Pradeep Mohan Kumar
K. Kottilingam
T. Karthick
P. Vijayakumar
P. Ganeshkumar
Publikationsdatum
03.12.2018
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 8/2019
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-018-3636-5

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