Journal of Ubiquitous Computing and Communication Technologies
IRO Journals
Volume 1 — Issue 1 — September 2019

DDOS ATTACK DETECTION IN TELECOMMUNICATION NETWORK USING MACHINE LEARNING

https://doi.org/10.36548/jucct.2019.1.004
A. Pasumpon Pandian
Professor, Computer Science Engineering, KGiSL Institute of Technology, Coimbatore, India
S. Smys
Professor, Department of Computer Science Engineering, RVS technical Campus, Coimbatore, India
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How to Cite

Pandian, A. Pasumpon, and S. Smys. 2019. “DDOS ATTACK DETECTION IN TELECOMMUNICATION NETWORK USING MACHINE LEARNING”. Journal of Ubiquitous Computing and Communication Technologies 1 (1): 33-44. https://doi.org/10.36548/jucct.2019.1.004.
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Keywords

— Telecommunication network
— machine learning
— distributed denial of service
— cyber threats
— detection accuracy
— neural network
— support vector machine
Published: 30-09-2019

Abstract

The telecommunication network that is the assemblage of the terminal nodes enables the whole to be connected. The swift progress in the telecommunication networks and the information technology has enabled a seamless connection and the capacity to store and communicate vast scale of information in the form of text and voice that are sensitive. This makes the telecommunication networks prey to multiple cyber-threats of which the DDOS (distributed denial of service) are the more predominant type of the cyber-threat causing the denial of the services to the users. So the paper utilizing the combination of the neural network and the support vector machine presents the detection and the classification method for the DDOS attacks in the telecommunication network. The performance evaluation using the network simulator-2 enables to have the enhanced detection accuracy for the proposed method.

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