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. |
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| 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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 02692821 |
| DOI: | 10.1007/s10462-026-11519-4 |
