CNN-LSTM: hybrid deep neural network for network intrusion detection system; a case

Halbouni, Asmaa Hani and Gunawan, Teddy Surya and Habaebi, Mohamed Hadi and Halbouni, Murad and Kartiwi, Mira and Ahmad, Robiah (2022) CNN-LSTM: hybrid deep neural network for network intrusion detection system; a case. IEEE Access, 10. pp. 99837-99849. E-ISSN 2169-3536

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Abstract

Network security becomes indispensable to our daily interactions and networks. As attackers continue to develop new types of attacks and the size of networks continues to grow, the need for an effective intrusion detection system has become critical. Numerous studies implemented machine learning algorithms to develop an effective IDS; however, with the advent of deep learning algorithms and artificial neural networks that can generate features automatically without human intervention, researchers began to rely on deep learning. In our research, we took advantage of the Convolutional Neural Network’s ability to extract spatial features and the Long Short-Term Memory Network’s ability to extract temporal features to create a hybrid intrusion detection system model. We added batch normalization and dropout layers to the model to increase its performance. Based on the binary and multiclass classification, the model was trained using three datasets: CIC-IDS 2017, UNSW-NB15, and WSN-DS. The confusion matrix determines the system’s effectiveness, which includes evaluation criteria such as accuracy, precision, detection rate, F1-score, and false alarm rate (FAR). The effectiveness of the proposed model was demonstrated by experimental results showing a high detection rate, high accuracy, and a relatively low FAR.

Item Type:	Article (Journal)
Uncontrolled Keywords:	Intrusion detection system, deep learning, convolutional neural network, long-short term memory, accuracy, false alarm rate, binary classification, multiclass classification.
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 Kulliyyah of Information and Communication Technology > Department of Information System Kulliyyah of Information and Communication Technology > Department of Information System
Depositing User:	Prof. Dr. Teddy Surya Gunawan
Date Deposited:	30 Sep 2022 10:42
Last Modified:	30 Sep 2022 10:47
URI:	http://irep.iium.edu.my/id/eprint/100333

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