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

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.

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Abstract

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.

Item Type:	Conference or Workshop Item (Invited Papers)
Additional Information:	International external collaboration: 1. UIN Sunan Gunung Djati, Bandung, Indonesia 2. Arab American University, Jenin, Palestine
Uncontrolled Keywords:	Convolutional neural network, Deep learning, Intrusion detection system.
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices > TK7885 Computer engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User:	Prof. Dr. Teddy Surya Gunawan
Date Deposited:	23 Dec 2022 11:45
Last Modified:	23 Dec 2022 11:46
URI:	http://irep.iium.edu.my/id/eprint/101869

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