DOIONLINE 
DOIONLINE Publish In |
International Journal of Advance Computational Engineering and Networking (IJACEN)-IJACEN |
 Journal Home Volume Issue |
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Issue |
Volume-7,Issue-11 ( Nov, 2019 ) | |||||||||
Paper Title |
A Scheme of Anomalous Detection based on Deep Learning for Load Balancing in Wireless Networks | |||||||||
Author Name |
Hye-Young Kim | |||||||||
Affilition |
Major in Game Software, School of Games, Hongik University | |||||||||
Pages |
25-27 | |||||||||
Abstract |
There are used to define patterns of malicious network loads, while anomalous detections is more suitable for detecting normal and anomalous network loads in wireless networks. The important goal of these issues is to recognize the anomalous detections for better preparation against future load balancing of wireless networks. The deep learning based object recognition approaches attracting more attention to the researcher due to the revolution of a neural network parts called deep learning. Therefore, we propose an agent Detectbot that processes anomalous detection for load balancing based on deep learning in this paper. Keywords - Deep Learning, Load Balancing, Anomalous Detection, Wireless Networks | |||||||||
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