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Data generative model to detect the anomalies for IDS imbalance CICIDS2017 dataset

The system of intrusion detection dataset enables machine learning to recognize attack activity in the network. The intrusion, however, is naturally imbalanced, most of the traffic is normal traffic. Moreover, a certain attack is more popular than others. Therefore, the number of cases is highly imb...

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Main Authors: Barkah, Azhari Shouni, Selamat, Siti Rahayu, Zainal Abidin, Zaheera, Wahyudi, Rizki
Format: Article
Language:English
Published: UIKTEN - Association for Information Communication Technology Education and Science 2023
Online Access:http://eprints.utem.edu.my/id/eprint/28142/2/0101704092023.pdf
http://eprints.utem.edu.my/id/eprint/28142/
https://www.temjournal.com/content/121/TEMJournalFebruary2023_80_89.pdf
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spelling my.utem.eprints.281422025-01-06T10:59:30Z http://eprints.utem.edu.my/id/eprint/28142/ Data generative model to detect the anomalies for IDS imbalance CICIDS2017 dataset Barkah, Azhari Shouni Selamat, Siti Rahayu Zainal Abidin, Zaheera Wahyudi, Rizki The system of intrusion detection dataset enables machine learning to recognize attack activity in the network. The intrusion, however, is naturally imbalanced, most of the traffic is normal traffic. Moreover, a certain attack is more popular than others. Therefore, the number of cases is highly imbalanced with the majority of attacks dominated by Distributed Denial of Services (DDoS), Denial of Service Hulk (DoS_Hulk), and PortScan more than 4.5% of attacks data. While the minority attack such as DoS_goldeneye, DoS_slowloris, DoS_slowhttptest, Web Attacks, Infiltration, Bot, and Heartbleed was only recorded in less than 1% of attack data. We propose data generative model (DGM) using the Conditional Generative Adversarial Network (CGAN) to improve the class of minorities of the IDS dataset. In this study, we tested the performance of the Data Generative Model based on CGAN in the CICIDS2017 dataset. There are new attacks in this dataset, including Bot, Web_attacks, Infiltration and Heartbleed. According to our experiments, the model successfully detect new attacks and improves the weighted f1-score by 99,92% compared to that of achievers by existing methods using the CICIDS2017 dataset. UIKTEN - Association for Information Communication Technology Education and Science 2023-02 Article PeerReviewed text en cc_by_nc_nd_4 http://eprints.utem.edu.my/id/eprint/28142/2/0101704092023.pdf Barkah, Azhari Shouni and Selamat, Siti Rahayu and Zainal Abidin, Zaheera and Wahyudi, Rizki (2023) Data generative model to detect the anomalies for IDS imbalance CICIDS2017 dataset. TEM Journal, 12 (1). pp. 80-89. ISSN 2217-8309 https://www.temjournal.com/content/121/TEMJournalFebruary2023_80_89.pdf 10.18421/TEM121-11
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
description The system of intrusion detection dataset enables machine learning to recognize attack activity in the network. The intrusion, however, is naturally imbalanced, most of the traffic is normal traffic. Moreover, a certain attack is more popular than others. Therefore, the number of cases is highly imbalanced with the majority of attacks dominated by Distributed Denial of Services (DDoS), Denial of Service Hulk (DoS_Hulk), and PortScan more than 4.5% of attacks data. While the minority attack such as DoS_goldeneye, DoS_slowloris, DoS_slowhttptest, Web Attacks, Infiltration, Bot, and Heartbleed was only recorded in less than 1% of attack data. We propose data generative model (DGM) using the Conditional Generative Adversarial Network (CGAN) to improve the class of minorities of the IDS dataset. In this study, we tested the performance of the Data Generative Model based on CGAN in the CICIDS2017 dataset. There are new attacks in this dataset, including Bot, Web_attacks, Infiltration and Heartbleed. According to our experiments, the model successfully detect new attacks and improves the weighted f1-score by 99,92% compared to that of achievers by existing methods using the CICIDS2017 dataset.
format Article
author Barkah, Azhari Shouni
Selamat, Siti Rahayu
Zainal Abidin, Zaheera
Wahyudi, Rizki
spellingShingle Barkah, Azhari Shouni
Selamat, Siti Rahayu
Zainal Abidin, Zaheera
Wahyudi, Rizki
Data generative model to detect the anomalies for IDS imbalance CICIDS2017 dataset
author_facet Barkah, Azhari Shouni
Selamat, Siti Rahayu
Zainal Abidin, Zaheera
Wahyudi, Rizki
author_sort Barkah, Azhari Shouni
title Data generative model to detect the anomalies for IDS imbalance CICIDS2017 dataset
title_short Data generative model to detect the anomalies for IDS imbalance CICIDS2017 dataset
title_full Data generative model to detect the anomalies for IDS imbalance CICIDS2017 dataset
title_fullStr Data generative model to detect the anomalies for IDS imbalance CICIDS2017 dataset
title_full_unstemmed Data generative model to detect the anomalies for IDS imbalance CICIDS2017 dataset
title_sort data generative model to detect the anomalies for ids imbalance cicids2017 dataset
publisher UIKTEN - Association for Information Communication Technology Education and Science
publishDate 2023
url http://eprints.utem.edu.my/id/eprint/28142/2/0101704092023.pdf
http://eprints.utem.edu.my/id/eprint/28142/
https://www.temjournal.com/content/121/TEMJournalFebruary2023_80_89.pdf
_version_ 1821007584473120768
score 13.226497

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