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Detection model for ambiguous intrusion using SMOTE and LSTM for network security

In today's interconnected world, networks play a crucial role. Consequently, network security has become increasingly vital. To ensure network security, various methods are employed, including digital signatures, firewalls, and intrusion detection. Among these methods, intrusion detection syste...

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Main Authors: Khalaf, Al-Ogaidi Ali Hameed, Mohamed, Raihani, Abdul Raziff, Abdul Rafiez
Format: Article
Language:English
English
Published: Semarak Ilmu Publishing 2024
Subjects:
T58.5 Information technology
Online Access:http://irep.iium.edu.my/115980/1/115980_Detection%20model%20for%20ambiguous%20intrusion.pdf
http://irep.iium.edu.my/115980/2/115980_Detection%20model%20for%20ambiguous%20intrusion_SCOPUS.pdf
http://irep.iium.edu.my/115980/
https://semarakilmu.com.my/journals/index.php/applied_sciences_eng_tech/article/view/3895
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spelling my.iium.irep.1159802024-11-22T07:27:13Z http://irep.iium.edu.my/115980/ Detection model for ambiguous intrusion using SMOTE and LSTM for network security Khalaf, Al-Ogaidi Ali Hameed Mohamed, Raihani Abdul Raziff, Abdul Rafiez T58.5 Information technology In today's interconnected world, networks play a crucial role. Consequently, network security has become increasingly vital. To ensure network security, various methods are employed, including digital signatures, firewalls, and intrusion detection. Among these methods, intrusion detection systems have gained significant popularity due to their ability to identify new attacks. However, the accuracy of these systems still requires further improvement. One of the challenges is the potential bias introduced by using imbalance datasets that contains more information on normal activities than on attacks. To address it, SMOTE method was proposed and additionally, the study explores the use of Long Short-Term Memory (LSTM) for classification purposes. The experiments are conducted using two datasets: UNSW NB-15 and CICIDS 2017. The results obtained demonstrate that the proposed methods achieve an accuracy of 96% with the UNSW NB-15 dataset and 99% with the CICIDS 2017 dataset. These findings indicate an improvement of 3% and 1% respectively compared to existing literature. Semarak Ilmu Publishing 2024-02-13 Article PeerReviewed application/pdf en http://irep.iium.edu.my/115980/1/115980_Detection%20model%20for%20ambiguous%20intrusion.pdf application/pdf en http://irep.iium.edu.my/115980/2/115980_Detection%20model%20for%20ambiguous%20intrusion_SCOPUS.pdf Khalaf, Al-Ogaidi Ali Hameed and Mohamed, Raihani and Abdul Raziff, Abdul Rafiez (2024) Detection model for ambiguous intrusion using SMOTE and LSTM for network security. Journal of Advanced Research in Applied Sciences and Engineering Technology, 39 (2). pp. 191-203. E-ISSN 2462-1943 https://semarakilmu.com.my/journals/index.php/applied_sciences_eng_tech/article/view/3895 10.37934/araset.39.2.191203
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
English
topic T58.5 Information technology
spellingShingle T58.5 Information technology
Khalaf, Al-Ogaidi Ali Hameed
Mohamed, Raihani
Abdul Raziff, Abdul Rafiez
Detection model for ambiguous intrusion using SMOTE and LSTM for network security
description In today's interconnected world, networks play a crucial role. Consequently, network security has become increasingly vital. To ensure network security, various methods are employed, including digital signatures, firewalls, and intrusion detection. Among these methods, intrusion detection systems have gained significant popularity due to their ability to identify new attacks. However, the accuracy of these systems still requires further improvement. One of the challenges is the potential bias introduced by using imbalance datasets that contains more information on normal activities than on attacks. To address it, SMOTE method was proposed and additionally, the study explores the use of Long Short-Term Memory (LSTM) for classification purposes. The experiments are conducted using two datasets: UNSW NB-15 and CICIDS 2017. The results obtained demonstrate that the proposed methods achieve an accuracy of 96% with the UNSW NB-15 dataset and 99% with the CICIDS 2017 dataset. These findings indicate an improvement of 3% and 1% respectively compared to existing literature.
format Article
author Khalaf, Al-Ogaidi Ali Hameed
Mohamed, Raihani
Abdul Raziff, Abdul Rafiez
author_facet Khalaf, Al-Ogaidi Ali Hameed
Mohamed, Raihani
Abdul Raziff, Abdul Rafiez
author_sort Khalaf, Al-Ogaidi Ali Hameed
title Detection model for ambiguous intrusion using SMOTE and LSTM for network security
title_short Detection model for ambiguous intrusion using SMOTE and LSTM for network security
title_full Detection model for ambiguous intrusion using SMOTE and LSTM for network security
title_fullStr Detection model for ambiguous intrusion using SMOTE and LSTM for network security
title_full_unstemmed Detection model for ambiguous intrusion using SMOTE and LSTM for network security
title_sort detection model for ambiguous intrusion using smote and lstm for network security
publisher Semarak Ilmu Publishing
publishDate 2024
url http://irep.iium.edu.my/115980/1/115980_Detection%20model%20for%20ambiguous%20intrusion.pdf
http://irep.iium.edu.my/115980/2/115980_Detection%20model%20for%20ambiguous%20intrusion_SCOPUS.pdf
http://irep.iium.edu.my/115980/
https://semarakilmu.com.my/journals/index.php/applied_sciences_eng_tech/article/view/3895
_version_ 1817841058034221056
score 13.223943

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