A Deep Learning Approach Using Optimized LSTM for Anomaly‐Based Network Intrusion Detection.
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| Title: | A Deep Learning Approach Using Optimized LSTM for Anomaly‐Based Network Intrusion Detection. |
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| Authors: | Dardouri, Samia1,2 (AUTHOR) s.dardouri@su.edu.sa, Binwal, Shikha (AUTHOR) sbinwal@wiley.com |
| Source: | Journal of Engineering (2314-4912). 5/4/2026, Vol. 2026, p1-13. 13p. |
| Subjects: | Long short-term memory, Intrusion detection systems (Computer security), Deep learning, Feature selection, Internet security, Anomaly detection (Computer security) |
| Abstract: | With the exponential rise in cyber threats, anomaly‐based network intrusion detection systems (NIDSs) have become critical for maintaining robust cybersecurity. This article proposes an optimized long short‐term memory (LSTM) deep learning (DL) model specifically designed to detect anomalies in network traffic. By leveraging temporal dependencies and sequential patterns, the model improves the detection of complex and evolving intrusion behaviors. To further enhance performance, the framework incorporates synthetic minority oversampling technique (SMOTE) and feature selection to address class imbalance and reduce dimensionality. Evaluation using the CICIDS2017 benchmark dataset demonstrates that the proposed model outperforms traditional machine learning (ML) algorithms in terms of accuracy, precision, recall, and F1‐score. The findings highlight the potential of advanced LSTM architectures in significantly enhancing the effectiveness of intelligent IDSs. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Engineering (2314-4912) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Abstract: | With the exponential rise in cyber threats, anomaly‐based network intrusion detection systems (NIDSs) have become critical for maintaining robust cybersecurity. This article proposes an optimized long short‐term memory (LSTM) deep learning (DL) model specifically designed to detect anomalies in network traffic. By leveraging temporal dependencies and sequential patterns, the model improves the detection of complex and evolving intrusion behaviors. To further enhance performance, the framework incorporates synthetic minority oversampling technique (SMOTE) and feature selection to address class imbalance and reduce dimensionality. Evaluation using the CICIDS2017 benchmark dataset demonstrates that the proposed model outperforms traditional machine learning (ML) algorithms in terms of accuracy, precision, recall, and F1‐score. The findings highlight the potential of advanced LSTM architectures in significantly enhancing the effectiveness of intelligent IDSs. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 23144904 |
| DOI: | 10.1155/je/2021709 |
