Cover Image

CNN-IDS: Convolutional Neural Network for network intrusion detection system

The field of information technology is undergoing a global revolution; information is exchanged globally. Such action requires the existence of an effective data and network protection system. IDS can provide security, protect the network from attacks and threats, and identify potential security bre...

Full description

Saved in:
Bibliographic Details
Main Authors: Halbouni, Asmaa Hani, Gunawan, Teddy Surya, Halbouni, Murad, Abdullah Assaig, Faisal Ahmed, Effendi, Mufid Ridlo, Ismail, Nanang
Format: Conference or Workshop Item
Language:English
English
Published: IEEE 2022
Subjects:
TK7885 Computer engineering
Online Access:http://irep.iium.edu.my/101869/7/101869_CNN-IDS_Convolutional%20nureal%20network%20for%20network%20Intrusion%20Detection%20System.pdf
http://irep.iium.edu.my/101869/13/101869_CNN-IDS%20Convolutional%20Neural%20Network%20for%20network%20intrusion%20detection%20system_SCOPUS.pdf
http://irep.iium.edu.my/101869/
https://icwt-seei.org/2022/conference-program/
https://doi.org/10.1109/ICWT55831.2022.9935478
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.iium.irep.101869
record_format dspace
spelling my.iium.irep.1018692022-12-23T03:46:43Z http://irep.iium.edu.my/101869/ CNN-IDS: Convolutional Neural Network for network intrusion detection system Halbouni, Asmaa Hani Gunawan, Teddy Surya Halbouni, Murad Abdullah Assaig, Faisal Ahmed Effendi, Mufid Ridlo Ismail, Nanang TK7885 Computer engineering The field of information technology is undergoing a global revolution; information is exchanged globally. Such action requires the existence of an effective data and network protection system. IDS can provide security, protect the network from attacks and threats, and identify potential security breaches. In this paper, we developed a convolutional neural network-based intrusion detection system that was evaluated using the CIC-IDS2017 dataset. For newly public datasets, our model aims to deliver a low false alarm rate, high accuracy, and a high detection rate. The model achieved a 99.55 percent detection rate and 0.12 FAR using CIC-IDS2017 multiclass classification. IEEE 2022-11-07 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/101869/7/101869_CNN-IDS_Convolutional%20nureal%20network%20for%20network%20Intrusion%20Detection%20System.pdf application/pdf en http://irep.iium.edu.my/101869/13/101869_CNN-IDS%20Convolutional%20Neural%20Network%20for%20network%20intrusion%20detection%20system_SCOPUS.pdf Halbouni, Asmaa Hani and Gunawan, Teddy Surya and Halbouni, Murad and Abdullah Assaig, Faisal Ahmed and Effendi, Mufid Ridlo and Ismail, Nanang (2022) CNN-IDS: Convolutional Neural Network for network intrusion detection system. In: 2022 8th International Conference on Wireless and Telematics (ICWT), 21-22 July 2022, Yogyakarta, Indonesia. https://icwt-seei.org/2022/conference-program/ https://doi.org/10.1109/ICWT55831.2022.9935478
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 TK7885 Computer engineering
spellingShingle TK7885 Computer engineering
Halbouni, Asmaa Hani
Gunawan, Teddy Surya
Halbouni, Murad
Abdullah Assaig, Faisal Ahmed
Effendi, Mufid Ridlo
Ismail, Nanang
CNN-IDS: Convolutional Neural Network for network intrusion detection system
description The field of information technology is undergoing a global revolution; information is exchanged globally. Such action requires the existence of an effective data and network protection system. IDS can provide security, protect the network from attacks and threats, and identify potential security breaches. In this paper, we developed a convolutional neural network-based intrusion detection system that was evaluated using the CIC-IDS2017 dataset. For newly public datasets, our model aims to deliver a low false alarm rate, high accuracy, and a high detection rate. The model achieved a 99.55 percent detection rate and 0.12 FAR using CIC-IDS2017 multiclass classification.
format Conference or Workshop Item
author Halbouni, Asmaa Hani
Gunawan, Teddy Surya
Halbouni, Murad
Abdullah Assaig, Faisal Ahmed
Effendi, Mufid Ridlo
Ismail, Nanang
author_facet Halbouni, Asmaa Hani
Gunawan, Teddy Surya
Halbouni, Murad
Abdullah Assaig, Faisal Ahmed
Effendi, Mufid Ridlo
Ismail, Nanang
author_sort Halbouni, Asmaa Hani
title CNN-IDS: Convolutional Neural Network for network intrusion detection system
title_short CNN-IDS: Convolutional Neural Network for network intrusion detection system
title_full CNN-IDS: Convolutional Neural Network for network intrusion detection system
title_fullStr CNN-IDS: Convolutional Neural Network for network intrusion detection system
title_full_unstemmed CNN-IDS: Convolutional Neural Network for network intrusion detection system
title_sort cnn-ids: convolutional neural network for network intrusion detection system
publisher IEEE
publishDate 2022
url http://irep.iium.edu.my/101869/7/101869_CNN-IDS_Convolutional%20nureal%20network%20for%20network%20Intrusion%20Detection%20System.pdf
http://irep.iium.edu.my/101869/13/101869_CNN-IDS%20Convolutional%20Neural%20Network%20for%20network%20intrusion%20detection%20system_SCOPUS.pdf
http://irep.iium.edu.my/101869/
https://icwt-seei.org/2022/conference-program/
https://doi.org/10.1109/ICWT55831.2022.9935478
_version_ 1753788183160553472
score 13.211869

Similar Items