A Highly Accurate Machine Learning Approach for Developing Wireless Sensor Network Middleware
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Authors
Alshinina, Remah
Elleithy, Khaled M.
Issue Date
2018-03-23
Type
Other
Language
en_US
Keywords
Generative adversarial networks (GAN) , Middleware , Wireless sensor networks (WSN)
Alternative Title
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. We introduced 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 discriminator (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. Results illustrate that the proposed 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.