Network Anomaly Detection via a Dynamic-Aware Online Transfer Learning.
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| Title: | Network Anomaly Detection via a Dynamic-Aware Online Transfer Learning. |
|---|---|
| Authors: | Gu, Huanan1 1113965312@qq.com, Feng, Quanxi2 fqx9904@163.com |
| Source: | IAENG International Journal of Computer Science. Apr2026, Vol. 53 Issue 4, p1509-1522. 14p. |
| Subjects: | Anomaly detection (Computer security), Online data processing, Change-point problems, Internet security, Feature selection, Knowledge transfer |
| Abstract: | In dynamic network environments, concept drift poses a critical challenge to streaming data analysis. Existing online transfer learning methods, constrained by static weight allocation, fixed feature spaces, and delayed drift detection, often struggle to achieve a balance between stability and adaptability. To overcome these limitations, this paper proposes a Dynamic-Aware Online Transfer Learning (DAOTL) model that integrates three synergistic mechanisms to enhance adaptability under evolving data distributions. First, a Dynamic Adaptive Weight Decay (DAWD) mechanism combines instantaneous classifier performance with lifecycle-aware decay, enabling the model to retain historical knowledge while rapidly adapting to abrupt drifts. Second, an Incremental Feature Optimization Framework (IFOF) dynamically adjusts feature subsets within a sliding window to alleviate performance degradation caused by sudden shifts in feature space. Third, a drift-event-triggered mechanism monitors ensemble prediction confidence and replaces fixed-window updates with data-driven adaptations, enabling timely responses to drift events. Experimental evaluations on NSL-KDD and CSE-CIC-IDS2018 datasets, which are widely used benchmarks for network anomaly detection, demonstrate that DAOTL improves multi-class classification accuracy by 6.08% and 3.77%, respectively, compared to baseline models, validating its effectiveness in achieving both adaptability and efficiency in dynamic environments. [ABSTRACT FROM AUTHOR] |
| Copyright of IAENG International Journal of Computer Science is the property of International Association of Engineers (IAENG) 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 |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 192720784 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Network Anomaly Detection via a Dynamic-Aware Online Transfer Learning. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Gu%2C+Huanan%22">Gu, Huanan</searchLink><relatesTo>1</relatesTo><i> 1113965312@qq.com</i><br /><searchLink fieldCode="AR" term="%22Feng%2C+Quanxi%22">Feng, Quanxi</searchLink><relatesTo>2</relatesTo><i> fqx9904@163.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IAENG+International+Journal+of+Computer+Science%22">IAENG International Journal of Computer Science</searchLink>. Apr2026, Vol. 53 Issue 4, p1509-1522. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Anomaly+detection+%28Computer+security%29%22">Anomaly detection (Computer security)</searchLink><br /><searchLink fieldCode="DE" term="%22Online+data+processing%22">Online data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Change-point+problems%22">Change-point problems</searchLink><br /><searchLink fieldCode="DE" term="%22Internet+security%22">Internet security</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br /><searchLink fieldCode="DE" term="%22Knowledge+transfer%22">Knowledge transfer</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In dynamic network environments, concept drift poses a critical challenge to streaming data analysis. Existing online transfer learning methods, constrained by static weight allocation, fixed feature spaces, and delayed drift detection, often struggle to achieve a balance between stability and adaptability. To overcome these limitations, this paper proposes a Dynamic-Aware Online Transfer Learning (DAOTL) model that integrates three synergistic mechanisms to enhance adaptability under evolving data distributions. First, a Dynamic Adaptive Weight Decay (DAWD) mechanism combines instantaneous classifier performance with lifecycle-aware decay, enabling the model to retain historical knowledge while rapidly adapting to abrupt drifts. Second, an Incremental Feature Optimization Framework (IFOF) dynamically adjusts feature subsets within a sliding window to alleviate performance degradation caused by sudden shifts in feature space. Third, a drift-event-triggered mechanism monitors ensemble prediction confidence and replaces fixed-window updates with data-driven adaptations, enabling timely responses to drift events. Experimental evaluations on NSL-KDD and CSE-CIC-IDS2018 datasets, which are widely used benchmarks for network anomaly detection, demonstrate that DAOTL improves multi-class classification accuracy by 6.08% and 3.77%, respectively, compared to baseline models, validating its effectiveness in achieving both adaptability and efficiency in dynamic environments. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IAENG International Journal of Computer Science is the property of International Association of Engineers (IAENG) 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: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 1509 Subjects: – SubjectFull: Anomaly detection (Computer security) Type: general – SubjectFull: Online data processing Type: general – SubjectFull: Change-point problems Type: general – SubjectFull: Internet security Type: general – SubjectFull: Feature selection Type: general – SubjectFull: Knowledge transfer Type: general Titles: – TitleFull: Network Anomaly Detection via a Dynamic-Aware Online Transfer Learning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Gu, Huanan – PersonEntity: Name: NameFull: Feng, Quanxi IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1819656X Numbering: – Type: volume Value: 53 – Type: issue Value: 4 Titles: – TitleFull: IAENG International Journal of Computer Science Type: main |
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