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An efficient anomaly intrusion detection method with evolutionary kernel neural network random weights

Internet security requirements are increasing due to the growth of internet usage. One of the most efficient approaches used to secure the usage of the internet from internal and external intruders is Intrusion Detection System (IDS). Considering that using a combination of ANN and EA can produce an...

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Main Authors: Sarvari, Samira, Mohd Sani, Nor Fazlida, Mohd Hanapi, Zurina, Abdullah @ Selimun, Mohd Taufik
Format: Article
Language:English
Published: Little Lion Scientific 2020
Online Access:http://psasir.upm.edu.my/id/eprint/87818/1/ABSTRACT.pdf
http://psasir.upm.edu.my/id/eprint/87818/
http://www.jatit.org/volumes/ninetyeight7.php
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spelling my.upm.eprints.878182022-06-14T06:31:06Z http://psasir.upm.edu.my/id/eprint/87818/ An efficient anomaly intrusion detection method with evolutionary kernel neural network random weights Sarvari, Samira Mohd Sani, Nor Fazlida Mohd Hanapi, Zurina Abdullah @ Selimun, Mohd Taufik Internet security requirements are increasing due to the growth of internet usage. One of the most efficient approaches used to secure the usage of the internet from internal and external intruders is Intrusion Detection System (IDS). Considering that using a combination of ANN and EA can produce an advanced technique to develop an efficient anomaly detection approach for IDS, several types of research have used ENN algorithms to detect the attacks. To enhance the efficiency of anomaly-based detection in terms of accuracy of classification, in this paper, the evolutionary kernel neural network random weight is proposed. This model is applied to the NSLKDD dataset, an improvement of the KDD Cup'99. The proposed method achieved 99.24% accuracy which shows that the novel algorithm suggested is more superior to existing ones as it provides the optimal overall efficiency. Little Lion Scientific 2020 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/87818/1/ABSTRACT.pdf Sarvari, Samira and Mohd Sani, Nor Fazlida and Mohd Hanapi, Zurina and Abdullah @ Selimun, Mohd Taufik (2020) An efficient anomaly intrusion detection method with evolutionary kernel neural network random weights. Journal of Theoretical and Applied Information Technology, 98 (7). 963 - 976. ISSN 1992-8645; ESSN: 1817-3195 http://www.jatit.org/volumes/ninetyeight7.php
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Internet security requirements are increasing due to the growth of internet usage. One of the most efficient approaches used to secure the usage of the internet from internal and external intruders is Intrusion Detection System (IDS). Considering that using a combination of ANN and EA can produce an advanced technique to develop an efficient anomaly detection approach for IDS, several types of research have used ENN algorithms to detect the attacks. To enhance the efficiency of anomaly-based detection in terms of accuracy of classification, in this paper, the evolutionary kernel neural network random weight is proposed. This model is applied to the NSLKDD dataset, an improvement of the KDD Cup'99. The proposed method achieved 99.24% accuracy which shows that the novel algorithm suggested is more superior to existing ones as it provides the optimal overall efficiency.
format Article
author Sarvari, Samira
Mohd Sani, Nor Fazlida
Mohd Hanapi, Zurina
Abdullah @ Selimun, Mohd Taufik
spellingShingle Sarvari, Samira
Mohd Sani, Nor Fazlida
Mohd Hanapi, Zurina
Abdullah @ Selimun, Mohd Taufik
An efficient anomaly intrusion detection method with evolutionary kernel neural network random weights
author_facet Sarvari, Samira
Mohd Sani, Nor Fazlida
Mohd Hanapi, Zurina
Abdullah @ Selimun, Mohd Taufik
author_sort Sarvari, Samira
title An efficient anomaly intrusion detection method with evolutionary kernel neural network random weights
title_short An efficient anomaly intrusion detection method with evolutionary kernel neural network random weights
title_full An efficient anomaly intrusion detection method with evolutionary kernel neural network random weights
title_fullStr An efficient anomaly intrusion detection method with evolutionary kernel neural network random weights
title_full_unstemmed An efficient anomaly intrusion detection method with evolutionary kernel neural network random weights
title_sort efficient anomaly intrusion detection method with evolutionary kernel neural network random weights
publisher Little Lion Scientific
publishDate 2020
url http://psasir.upm.edu.my/id/eprint/87818/1/ABSTRACT.pdf
http://psasir.upm.edu.my/id/eprint/87818/
http://www.jatit.org/volumes/ninetyeight7.php
_version_ 1736835699998457856
score 13.211869

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