Boosting Intrusion Detection Accuracy With a Multibranch Deep Learning Framework.
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| Title: | Boosting Intrusion Detection Accuracy With a Multibranch Deep Learning Framework. |
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| Authors: | Li, Yang1 (AUTHOR) liyang@jsit.edu.cn, Bansal, Shonak1 (AUTHOR) shonakk@gmail.com |
| Source: | Journal of Electrical & Computer Engineering. 6/16/2026, Vol. 2026, p1-12. 12p. |
| Subjects: | Intrusion detection systems (Computer security), Deep learning, Feedforward neural networks, Data fusion (Statistics) |
| Abstract: | To overcome the limitations of traditional intrusion detection methods in dealing with high‐dimensional sparse features, multiclass attack classification, and model robustness, this paper presents a fused multibranch intrusion detection model (FMB‐IDM). The proposed framework combines three complementary deep learning components. First, an MLP branch equipped with a Squeeze‐and‐Excitation attention module is used to enhance feature representation by adaptively reweighting channel‐wise information. Second, a TabTransformer‐based branch is introduced to model complex dependencies among structured input features through self‐attention. Third, a CNN‐BiLSTM branch is employed to capture both local feature patterns and longer‐range sequential relationships, where the CNN extracts local representations, and the two‐layer bidirectional LSTM learns contextual dependencies. To make better use of the information learned by each branch, an attention‐based fusion strategy is adopted to combine their outputs adaptively, which improves the model's ability to distinguish different types of intrusion behaviors. Experiments conducted on the NSL‐KDD, UNSW‐NB15, and CIC‐IDS2017 datasets demonstrate that the proposed model achieves strong and consistent performance in both binary and multiclass intrusion detection tasks. In particular, on CIC‐IDS2017, FMB‐IDM reaches an accuracy of 97.56% and a weighted F1‐score of 0.98. In addition, the model maintains good inference efficiency, with an average latency of about 0.03 ms per sample under the experimental hardware configuration. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Electrical & Computer Engineering 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: 194609706 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Boosting Intrusion Detection Accuracy With a Multibranch Deep Learning Framework. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Li%2C+Yang%22">Li, Yang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> liyang@jsit.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Bansal%2C+Shonak%22">Bansal, Shonak</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> shonakk@gmail.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Electrical+%26+Computer+Engineering%22">Journal of Electrical & Computer Engineering</searchLink>. 6/16/2026, Vol. 2026, p1-12. 12p. – Name: Subject Label: Subjects Group: Su Data: <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="%22Feedforward+neural+networks%22">Feedforward neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Data+fusion+%28Statistics%29%22">Data fusion (Statistics)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: To overcome the limitations of traditional intrusion detection methods in dealing with high‐dimensional sparse features, multiclass attack classification, and model robustness, this paper presents a fused multibranch intrusion detection model (FMB‐IDM). The proposed framework combines three complementary deep learning components. First, an MLP branch equipped with a Squeeze‐and‐Excitation attention module is used to enhance feature representation by adaptively reweighting channel‐wise information. Second, a TabTransformer‐based branch is introduced to model complex dependencies among structured input features through self‐attention. Third, a CNN‐BiLSTM branch is employed to capture both local feature patterns and longer‐range sequential relationships, where the CNN extracts local representations, and the two‐layer bidirectional LSTM learns contextual dependencies. To make better use of the information learned by each branch, an attention‐based fusion strategy is adopted to combine their outputs adaptively, which improves the model's ability to distinguish different types of intrusion behaviors. Experiments conducted on the NSL‐KDD, UNSW‐NB15, and CIC‐IDS2017 datasets demonstrate that the proposed model achieves strong and consistent performance in both binary and multiclass intrusion detection tasks. In particular, on CIC‐IDS2017, FMB‐IDM reaches an accuracy of 97.56% and a weighted F1‐score of 0.98. In addition, the model maintains good inference efficiency, with an average latency of about 0.03 ms per sample under the experimental hardware configuration. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Electrical & Computer Engineering 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/jece/2865894 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 1 Subjects: – SubjectFull: Intrusion detection systems (Computer security) Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Feedforward neural networks Type: general – SubjectFull: Data fusion (Statistics) Type: general Titles: – TitleFull: Boosting Intrusion Detection Accuracy With a Multibranch Deep Learning Framework. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Li, Yang – PersonEntity: Name: NameFull: Bansal, Shonak IsPartOfRelationships: – BibEntity: Dates: – D: 16 M: 06 Text: 6/16/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20900147 Numbering: – Type: volume Value: 2026 Titles: – TitleFull: Journal of Electrical & Computer Engineering Type: main |
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