DDos Detection and Mitigation SDN using support vector machine

Supplementary Files

PDF

Keywords

DDoS attack
Software-defined networking
Support vector machine
Network security; Machine learning

Categories

How to Cite

DDos Detection and Mitigation SDN using support vector machine. (2021). International Journal of Advanced Scientific Innovation, 1(2), 26-31. https://doi.org/10.5281/zenodo.4782280

Abstract

In recent years, the rise of software-defined
networks (SDN) have made network control more
flexible, easier to set up and manage, and have provided a
stronger ability to adapt to the changing demands of
application development and network conditions. The
the network becomes easier to maintain but also achieves
improved security as a result of SDN. The architecture of
SDN is designed for Control Plane and Forwarding
Plane separation and uses open APIs to realize
programmable control. SDN allows for the importing of
third- party applications to improve network service, or
even provide a new network service. In this paper, we
present a defense mechanism, which can find an attack
packets previously identified through the Sniffer function,
and once the abnormal flow is found, the protection
mechanism of the Firewall function will be activated. For
the capture of the packets, available libraries will be
used to determine the properties and contents of the
malicious packet, and to anticipate any possible attacks.
Through the prediction of all latent malicious behaviors,
our new defense algorithm can prevent potential losses
like system failures or crashes and reduce the risk of
being attacked

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Copyright (c) 2021 Prajwal S, Siddhartha M, Charan S