To Achieve an Unified Intrusion Detection System Based on Artificial Neural Network

Authors(2) :-Dr. Abid Hussain, Mr. Praveen Kumar Sharma

An intrusion detection system is an important component of the computer and information security framework. Its main goal is to differentiate between normal activities of the system and behaviours that can be classified as suspicious or intrusive. The research aims at the design, implementation and evaluation of an intelligent Intrusion Detection System based on artificial neural network that can promptly detect attacks, no matter they are known or not. In this system, neural network is used to learn about the normal users' behaviour to form the network traffic that only contains information about normal users. When the learning is over, the system is tested with the network traffic that contains both attacks and normal data. A simulated computer network is used to test the system performance. In experiments, system performance has been compared with other research works and the results in experiments are very promising.

Authors and Affiliations

Dr. Abid Hussain
Assistant Professor, School of Computer Applications, Career Point University, Kota, Rajasthan, India
Mr. Praveen Kumar Sharma
Vardhman Mahaveer Open University, Kota, Rajasthan, India

Anomaly-based system; Network security, Intrusion Detection System, Artificial Neural Network.

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Publication Details

Published in : Volume 3 | Issue 7 | September-October 2018
Date of Publication : 2018-09-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 108-114
Manuscript Number : CSEIT183715
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Dr. Abid Hussain, Mr. Praveen Kumar Sharma, "To Achieve an Unified Intrusion Detection System Based on Artificial Neural Network", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 7, pp.108-114, September-October-2018.
Journal URL : http://ijsrcseit.com/CSEIT183715

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