Developing an attack detection framework for wireless sensor network‐based healthcare applications using hybrid convolutional neural network

Author(s):  
C. A. Subasini ◽  
S. P. Karuppiah ◽  
Adlin Sheeba ◽  
S. Padmakala

Over the recent years, the term deep learning has been considered as one of the primary choice for handling huge amount of data. Having deeper hidden layers, it surpasses classical methods for detection of outlier in wireless sensor network. The Convolutional Neural Network (CNN) is a biologically inspired computational model which is one of the most popular deep learning approaches. It comprises neurons that self-optimize through learning. EEG generally known as Electroencephalography is a tool used for investigation of brain function and EEG signal gives time-series data as output. In this paper, we propose a state-of-the-art technique designed by processing the time-series data generated by the sensor nodes stored in a large dataset into discrete one-second frames and these frames are projected onto a 2D map images. A convolutional neural network (CNN) is then trained to classify these frames. The result improves detection accuracy and encouraging.


PLoS ONE ◽  
2018 ◽  
Vol 13 (5) ◽  
pp. e0196251 ◽  
Author(s):  
Jungmo Ahn ◽  
JaeYeon Park ◽  
Donghwan Park ◽  
Jeongyeup Paek ◽  
JeongGil Ko

2021 ◽  
pp. 315-323
Author(s):  
Thi-Kien Dao ◽  
Trong-The Nguyen ◽  
Van-Dinh Vu ◽  
Truong-Giang Ngo

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