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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| Header | DbId: egs DbLabel: Engineering Source An: 193491110 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Deep Learning Approach Using Optimized LSTM for Anomaly‐Based Network Intrusion Detection. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Dardouri%2C+Samia%22">Dardouri, Samia</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> s.dardouri@su.edu.sa</i><br /><searchLink fieldCode="AR" term="%22Binwal%2C+Shikha%22">Binwal, Shikha</searchLink> (AUTHOR)<i> sbinwal@wiley.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Engineering+%282314-4912%29%22">Journal of Engineering (2314-4912)</searchLink>. 5/4/2026, Vol. 2026, p1-13. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Long+short-term+memory%22">Long short-term memory</searchLink><br /><searchLink fieldCode="DE" term="%22Intrusion+detection+systems+%28Computer+security%29%22">Intrusion detection systems (Computer security)</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br /><searchLink fieldCode="DE" term="%22Internet+security%22">Internet security</searchLink><br /><searchLink fieldCode="DE" term="%22Anomaly+detection+%28Computer+security%29%22">Anomaly detection (Computer security)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>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.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1155/je/2021709 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 1 Subjects: – SubjectFull: Long short-term memory Type: general – SubjectFull: Intrusion detection systems (Computer security) Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Feature selection Type: general – SubjectFull: Internet security Type: general – SubjectFull: Anomaly detection (Computer security) Type: general Titles: – TitleFull: A Deep Learning Approach Using Optimized LSTM for Anomaly‐Based Network Intrusion Detection. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Dardouri, Samia – PersonEntity: Name: NameFull: Binwal, Shikha IsPartOfRelationships: – BibEntity: Dates: – D: 04 M: 05 Text: 5/4/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 23144904 Numbering: – Type: volume Value: 2026 Titles: – TitleFull: Journal of Engineering (2314-4912) Type: main |
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