Improving Intrusion Detection Systems with Federated Learning: A Comprehensive Review

What is it about?

This paper explores how a new technology called Federated Learning (FL) can improve the way we detect threats in computer networks. Traditional systems that identify these threats often struggle because they need large amounts of data, which can be hard to share due to privacy concerns. Federated Learning allows multiple organizations to work together and build stronger threat detection models without sharing their actual data. Instead, each organization trains a part of the model using their data and only shares the updates, not the data itself. This method helps keep information private while improving the overall system's ability to detect threats. Our survey reviews the latest approaches, challenges, and future directions for using FL in network security.

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Photo by Growtika on Unsplash

Photo by Growtika on Unsplash

Why is it important?

This work is crucial because cyber threats are becoming more advanced, and traditional detection systems often can't keep up due to limitations in accessing and sharing data. What makes this survey unique is its focus on Federated Learning—a cutting-edge method that allows organizations to collaborate and enhance threat detection capabilities without compromising data privacy. By highlighting the most recent advancements, challenges, and practical solutions, this survey provides a comprehensive resource for researchers and cybersecurity professionals. It helps them understand how Federated Learning can be applied to create stronger and more secure network defenses. This timely exploration is key for developing next-generation cybersecurity systems that protect data without sacrificing privacy.

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