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Survey on Federated Learning for Intrusion Detection System: Concept, Architectures, Aggregation Strategies, Challenges, and Future Directions

Published: 07 October 2024 Publication History

Abstract

Intrusion Detection Systems (IDS) are essential for securing computer networks by identifying and mitigating potential threats. However, traditional IDS face challenges related to scalability, privacy, and computational demands as network data complexity increases. Federated Learning (FL) has emerged as a promising solution, enabling collaborative model training on decentralized data sources while preserving data privacy. Each participant retains local data repositories, ensuring data sovereignty and precluding data sharing. Leveraging the FL framework, participants locally train machine learning models on their respective datasets, subsequently transmitting model updates to a central server for aggregation. The central server then disseminates the aggregated model updates to individual participants, collectively striving to bolster intrusion detection capabilities. This article presents a comprehensive survey of FL applications in IDS, covering core concepts, architectural approaches, and aggregation strategies. We evaluate the strengths and limitations of various FL methodologies for IDS, addressing privacy and security concerns and exploring privacy-preserving techniques and security protocols. Our examination of aggregation strategies within the FL framework for IDS aims to highlight their effectiveness, limitations, and potential enhancements.

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cover image ACM Computing Surveys
ACM Computing Surveys  Volume 57, Issue 1
January 2025
984 pages
EISSN:1557-7341
DOI:10.1145/3696794
  • Editors:
  • David Atienza,
  • Michela Milano
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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 October 2024
Online AM: 06 August 2024
Accepted: 01 August 2024
Revised: 30 June 2024
Received: 08 September 2023
Published in CSUR Volume 57, Issue 1

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  1. Intrusion detection systems
  2. federated learning
  3. privacy preservation
  4. network security

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