Journal
IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT
Volume 19, Issue 3, Pages 2309-2332Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNSM.2022.3177512
Keywords
Collaboration; Taxonomy; Statistical analysis; Organizations; Collaborative work; Standardization; Semantics; Federated learning; machine learning; intrusion detection systems; collaborative sharing; network security management; attack mitigation
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Funding
- FEDER development fund of the Brittany region
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This paper focuses on the application of Federated Learning (FL) to Intrusion Detection Systems (IDSs) and provides a comprehensive survey of the state of the art in this field.
In 2016, Google introduced the concept of Federated Learning (FL), enabling collaborative Machine Learning (ML). FL does not share local data but ML models, offering applications in diverse domains. This paper focuses on the application of FL to Intrusion Detection Systems (IDSs). There, common criteria to compare existing solutions are missing. In particular, this survey shows: (i) how FL-based IDSs are used in different domains; (ii) what differences exist between architectures; (iii) the state of the art of FL-based IDS. With a structured literature survey, this work identifies the relevant state of the art in FL-based intrusion detection from its creation in 2016 until 2021. It provides a reference architecture and a taxonomy to serve as guidelines to compare and design FL-based IDSs. Both are validated with the existing works. Finally, it identifies research directions for the application of FL to intrusion detection systems.
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