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Detection of distributed denial-of-service (DDoS) attack with hyperparameter tuning based on machine learning approach

Distributed Denial-of-Service (DDoS) attack is a malicious cyber-attack which targets availability element in CIA triad and to disrupt the availability of network services of a target by performing a huge malicious traffic flood. To conduct the study, a standard benchmark dataset DDoS Attack SDN Dat...

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Main Authors: Wan Nurulsafawati, Wan Manan, Choo, Yong Han
Format: Conference or Workshop Item
Language:English
English
Published: IEEE 2023
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://umpir.ump.edu.my/id/eprint/40851/1/Detection%20of%20distributed%20denial-of-service_ABST.pdf
http://umpir.ump.edu.my/id/eprint/40851/2/Detection%20of%20Distributed%20Denial-of-Service.pdf
http://umpir.ump.edu.my/id/eprint/40851/
https://doi.org/10.1109/ISAS60782.2023.10391487
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spelling my.ump.umpir.408512024-04-03T02:35:19Z http://umpir.ump.edu.my/id/eprint/40851/ Detection of distributed denial-of-service (DDoS) attack with hyperparameter tuning based on machine learning approach Wan Nurulsafawati, Wan Manan Choo, Yong Han QA75 Electronic computers. Computer science Distributed Denial-of-Service (DDoS) attack is a malicious cyber-attack which targets availability element in CIA triad and to disrupt the availability of network services of a target by performing a huge malicious traffic flood. To conduct the study, a standard benchmark dataset DDoS Attack SDN Dataset is applied. EDA and Data Pre-processing are performed to ensure a clean dataset is produced for obtaining an accurate and meaningful detection performance results. Hyperparameter tuning is performed to enhance the detection performance of the models. It is proposed that DNN shows the promising results as it has shown 99.84% accuracy to detect DDoS attack after performing hyperparameter tuning. It is observed that hyperparameter tuning has improved and increased most of the performance results of DNN and DT, with increment 4.84% in DT while 0.97% in DNN. Besides, the detection results have been increased and their false detection has been reduced. This study could help to reduce the dwell time of DDoS attack, increase the Mean Time To Contain (MTTC) and avoid alarm fatigue. IEEE 2023 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/40851/1/Detection%20of%20distributed%20denial-of-service_ABST.pdf pdf en http://umpir.ump.edu.my/id/eprint/40851/2/Detection%20of%20Distributed%20Denial-of-Service.pdf Wan Nurulsafawati, Wan Manan and Choo, Yong Han (2023) Detection of distributed denial-of-service (DDoS) attack with hyperparameter tuning based on machine learning approach. In: 7th International Symposium on Innovative Approaches in Smart Technologies (ISAS 2023) , 23 - 25 November 2023 , Istanbul. pp. 1-8. (196776). ISBN 979-835038306-5 https://doi.org/10.1109/ISAS60782.2023.10391487
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Wan Nurulsafawati, Wan Manan
Choo, Yong Han
Detection of distributed denial-of-service (DDoS) attack with hyperparameter tuning based on machine learning approach
description Distributed Denial-of-Service (DDoS) attack is a malicious cyber-attack which targets availability element in CIA triad and to disrupt the availability of network services of a target by performing a huge malicious traffic flood. To conduct the study, a standard benchmark dataset DDoS Attack SDN Dataset is applied. EDA and Data Pre-processing are performed to ensure a clean dataset is produced for obtaining an accurate and meaningful detection performance results. Hyperparameter tuning is performed to enhance the detection performance of the models. It is proposed that DNN shows the promising results as it has shown 99.84% accuracy to detect DDoS attack after performing hyperparameter tuning. It is observed that hyperparameter tuning has improved and increased most of the performance results of DNN and DT, with increment 4.84% in DT while 0.97% in DNN. Besides, the detection results have been increased and their false detection has been reduced. This study could help to reduce the dwell time of DDoS attack, increase the Mean Time To Contain (MTTC) and avoid alarm fatigue.
format Conference or Workshop Item
author Wan Nurulsafawati, Wan Manan
Choo, Yong Han
author_facet Wan Nurulsafawati, Wan Manan
Choo, Yong Han
author_sort Wan Nurulsafawati, Wan Manan
title Detection of distributed denial-of-service (DDoS) attack with hyperparameter tuning based on machine learning approach
title_short Detection of distributed denial-of-service (DDoS) attack with hyperparameter tuning based on machine learning approach
title_full Detection of distributed denial-of-service (DDoS) attack with hyperparameter tuning based on machine learning approach
title_fullStr Detection of distributed denial-of-service (DDoS) attack with hyperparameter tuning based on machine learning approach
title_full_unstemmed Detection of distributed denial-of-service (DDoS) attack with hyperparameter tuning based on machine learning approach
title_sort detection of distributed denial-of-service (ddos) attack with hyperparameter tuning based on machine learning approach
publisher IEEE
publishDate 2023
url http://umpir.ump.edu.my/id/eprint/40851/1/Detection%20of%20distributed%20denial-of-service_ABST.pdf
http://umpir.ump.edu.my/id/eprint/40851/2/Detection%20of%20Distributed%20Denial-of-Service.pdf
http://umpir.ump.edu.my/id/eprint/40851/
https://doi.org/10.1109/ISAS60782.2023.10391487
_version_ 1822924257889353728
score 13.235362

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