A survey of privacy-preserving federated learning for intrusion detection systems.
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| Title: | A survey of privacy-preserving federated learning for intrusion detection systems. |
|---|---|
| Authors: | Bunko, Thomas1 (AUTHOR) tbunko@our.ecu.edu.au, Johnstone, Michael N.1 (AUTHOR) m.johnstone@ecu.edu.au, Yang, Wencheng2 (AUTHOR) wencheng.yang@unisq.edu.au, Scott, Ben A.3,4 (AUTHOR) ben.scott@mq.edu.au |
| Source: | Artificial Intelligence Review. May2026, Vol. 59 Issue 5, p1-47. 47p. |
| Subjects: | Intrusion detection systems (Computer security), Federated learning, Adversarial machine learning, Internet security, Data encryption, Data security failures, Data privacy |
| Abstract: | Intrusion detection systems (IDS) monitor and detect malicious activity and unauthorized access that may compromise systems. Traditional IDS approaches send data to a central server for analysis, raising privacy concerns as data owners lose control over security. Federated Learning (FL) offers a privacy-preserving alternative by allowing local devices to process their data and generate models without sharing raw data. These local models are aggregated centrally to form a comprehensive model with performance comparable to centralized systems. This paper reviews FL-based IDS research, and is the first review paper to focus on privacy-preserving techniques collectively known as privacy-preserving Federated Learning (PPFL) for IDS. We examine methods used to prevent data leakage while maintaining detection effectiveness, including encryption-based and lightweight alternatives. While FL keeps raw data local, it remains susceptible to inference and poisoning attacks. Our findings show that most FL-based IDS research concentrates on data locality alone, with limited adoption of additional privacy-enhancing techniques. Advancing PPFL-IDS requires moving beyond data while addressing trade-offs. This review highlights key gaps and directions for future research. [ABSTRACT FROM AUTHOR] |
| Copyright of Artificial Intelligence Review is the property of Springer Nature 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: 192482050 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A survey of privacy-preserving federated learning for intrusion detection systems. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Bunko%2C+Thomas%22">Bunko, Thomas</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> tbunko@our.ecu.edu.au</i><br /><searchLink fieldCode="AR" term="%22Johnstone%2C+Michael+N%2E%22">Johnstone, Michael N.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> m.johnstone@ecu.edu.au</i><br /><searchLink fieldCode="AR" term="%22Yang%2C+Wencheng%22">Yang, Wencheng</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> wencheng.yang@unisq.edu.au</i><br /><searchLink fieldCode="AR" term="%22Scott%2C+Ben+A%2E%22">Scott, Ben A.</searchLink><relatesTo>3,4</relatesTo> (AUTHOR)<i> ben.scott@mq.edu.au</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Artificial+Intelligence+Review%22">Artificial Intelligence Review</searchLink>. May2026, Vol. 59 Issue 5, p1-47. 47p. – 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="%22Federated+learning%22">Federated learning</searchLink><br /><searchLink fieldCode="DE" term="%22Adversarial+machine+learning%22">Adversarial machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Internet+security%22">Internet security</searchLink><br /><searchLink fieldCode="DE" term="%22Data+encryption%22">Data encryption</searchLink><br /><searchLink fieldCode="DE" term="%22Data+security+failures%22">Data security failures</searchLink><br /><searchLink fieldCode="DE" term="%22Data+privacy%22">Data privacy</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Intrusion detection systems (IDS) monitor and detect malicious activity and unauthorized access that may compromise systems. Traditional IDS approaches send data to a central server for analysis, raising privacy concerns as data owners lose control over security. Federated Learning (FL) offers a privacy-preserving alternative by allowing local devices to process their data and generate models without sharing raw data. These local models are aggregated centrally to form a comprehensive model with performance comparable to centralized systems. This paper reviews FL-based IDS research, and is the first review paper to focus on privacy-preserving techniques collectively known as privacy-preserving Federated Learning (PPFL) for IDS. We examine methods used to prevent data leakage while maintaining detection effectiveness, including encryption-based and lightweight alternatives. While FL keeps raw data local, it remains susceptible to inference and poisoning attacks. Our findings show that most FL-based IDS research concentrates on data locality alone, with limited adoption of additional privacy-enhancing techniques. Advancing PPFL-IDS requires moving beyond data while addressing trade-offs. This review highlights key gaps and directions for future research. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Artificial Intelligence Review is the property of Springer Nature 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: 10.1007/s10462-026-11519-4 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 47 StartPage: 1 Subjects: – SubjectFull: Intrusion detection systems (Computer security) Type: general – SubjectFull: Federated learning Type: general – SubjectFull: Adversarial machine learning Type: general – SubjectFull: Internet security Type: general – SubjectFull: Data encryption Type: general – SubjectFull: Data security failures Type: general – SubjectFull: Data privacy Type: general Titles: – TitleFull: A survey of privacy-preserving federated learning for intrusion detection systems. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Bunko, Thomas – PersonEntity: Name: NameFull: Johnstone, Michael N. – PersonEntity: Name: NameFull: Yang, Wencheng – PersonEntity: Name: NameFull: Scott, Ben A. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 02692821 Numbering: – Type: volume Value: 59 – Type: issue Value: 5 Titles: – TitleFull: Artificial Intelligence Review Type: main |
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