Paper Info
Title
A highly accurate machine learning approach for developing wireless sensor network middleware
Abstract
Despite the popularity of wireless sensor networks (WSNs) in a wide range of applications, security problems associated with them have not been completely resolved. Middleware is generally introduced as an intermediate layer between WSNs and the end user to resolve some limitations, but most of the existing middleware is unable to protect data from malicious and unknown attacks during transmission. This paper introduces an intelligent middleware based on an unsupervised learning technique called Generative Adversarial Networks (GANs) algorithm. GANs contain two networks: a generator (G) network and a detector (D) network. The G creates fake data similar to the real samples and combines it with real data from the sensors to confuse the attacker. The D contains multi-layers that have the ability to differentiate between real and fake data. The output intended for this algorithm shows an actual interpretation of the data that is securely communicated through the WSN. The framework is implemented in Python with experiments performed using Keras. Results illustrate that the suggested algorithm not only improves the accuracy of the data but also enhances its security by protecting data from adversaries. Data transmission from the WSN to the end user then becomes much more secure and accurate compared to conventional techniques.
Year
DOI
Venue
2018
10.1109/WTS.2018.8363955
2018 Wireless Telecommunications Symposium (WTS)
Keywords
Field
DocType
Middleware,unsupervised learning,WSNs,generator,detector,visualization,confusion matrix,security,GANs
Middleware,Confusion matrix,End user,Data transmission,Computer science,Visualization,Computer network,Unsupervised learning,Wireless sensor network,Python (programming language)
Conference
ISSN
ISBN
Citations 
1934-5070
978-1-5386-3396-0
0
PageRank 
References 
Authors
0.34
13
2
Name
Order
Citations
PageRank
Remah Alshinina191.52
Khaled M. Elleithy244872.86