Optimized Intrusion Detection System using Deep Learning Algorithm

K. Veena; Dr N. Suguna; Prof P. Damodharan

Title:
Optimized Intrusion Detection System using Deep Learning Algorithm

Authors:
Prof P. Damodharan | K. Veena | Dr N. Suguna
DOI:
https://doi.org/10.31142/ijtsrd21447

Cite This Article :

Prof P. Damodharan | K. Veena | Dr N. Suguna "Optimized Intrusion Detection System using Deep Learning Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-2, February 2019, pp.528-534, URL: https://www.ijtsrd.com/papers/ijtsrd21447.pdf

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Abstract :
A method and a system for the detection of an intrusion in a computer network compare the network traffic of the computer network at multiple different points in the network. In an uncompromised network the network traffic monitored at these two different points in the network should be identical. A network intrusion detection system is mostly place at strategic points in a network, so that it can monitor the traffic traveling to or from different devices on that network. The existing Software Defined Network (SDN) proposes the separation of forward and control planes by introducing a new independent plane called network controller. Machine learning is an artificial intelligence approach that focuses on acquiring knowledge from raw data and, based at least in part on the identified flow, selectively causing the packet, or a packet descriptor associated with the packet. The performance is evaluated using the network analysis metrics such as key generation delay, key sharing delay and the hash code generation time for both SDN and the proposed machine learning SDN.

Keywords :
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Publication Details:

Unique Identification Number : IJTSRD21447

Published In : Volume-3 | Issue-2, February 2019

Page Number(s) : 528-534

Publisher Name : IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470

Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

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