An innovative network intrusion detection system (NIDS): Hierarchical deep learning model based on Unsw-Nb15 dataset

Alsharaiah, M.; Abualhaj, M.; Baniata, L.; Al-saaidah, A.; Kharma, Q.; Al-Zyoud, M.

Growing Science » International Journal of Data and Network Science » An innovative network intrusion detection system (NIDS): Hierarchical deep learning model based on Unsw-Nb15 dataset

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International Journal of Data and Network Science

ISSN 2561-8156 (Online) - ISSN 2561-8148 (Print) Quarterly Publication Volume 8 Issue 2 pp. 709-722 , 2024

An innovative network intrusion detection system (NIDS): Hierarchical deep learning model based on Unsw-Nb15 dataset Pages 709-722 Download PDF

Authors: Mohammad A. Alsharaiah, Mosleh Abualhaj, Laith H. Baniata, Adeeb Al-saaidah, Qasem M. Kharma, Mahran M Al-Zyoud

DOI: 10.5267/j.ijdns.2024.1.007

Keywords: UNSW-NB15, Classification, Machine learning, Deep learning, LSTM attention

Abstract: With the increasing prevalence of network intrusions, the development of effective network intrusion detection systems (NIDS) has become crucial. In this study, we propose a novel NIDS approach that combines the power of long short-term memory (LSTM) and attention mechanisms to analyze the spatial and temporal features of network traffic data. We utilize the benchmark UNSW-NB15 dataset, which exhibits a diverse distribution of patterns, including a significant disparity in the size of the training and testing sets. Unlike traditional machine learning techniques like support vector machines (SVM) and k-nearest neighbors (KNN) that often struggle with limited feature sets and lower accuracy, our proposed model overcomes these limitations. Notably, existing models applied to this dataset typically require manual feature selection and extraction, which can be time-consuming and less precise. In contrast, our model achieves superior results in binary classification by leveraging the advantages of LSTM and attention mechanisms. Through extensive experiments and evaluations with state-of-the-art ML/DL models, we demonstrate the effectiveness and superiority of our proposed approach. Our findings highlight the potential of combining LSTM and attention mechanisms for enhanced network intrusion detection.

How to cite this paper

 Alsharaiah, M., Abualhaj, M., Baniata, L., Al-saaidah, A., Kharma, Q & Al-Zyoud, M. (2024). An innovative network intrusion detection system (NIDS): Hierarchical deep learning model based on Unsw-Nb15 dataset.International Journal of Data and Network Science, 8(2), 709-722.

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