Papers › Intrusion Detection with Segmented Federated Learning for Large-Scale Multiple LANs

Intrusion Detection with Segmented Federated Learning for Large-Scale Multiple LANs

28 Sep 2020International Joint Conference on Neural Networks (IJCNN) 2020 9archive 2025-07-28

Yuwei Sun, Hideya Ochiai, Hiroshi Esaki

Traditional approaches to cybersecurity issues usually protect users from attacks after the occurrence of specific types of attacks. Besides, patterns of recent cyberattacks tend to be changeable, which add up to unpredictability of them. On the other hand, machine learning, as a new method used to detect intrusion, is attracting more and more attention. Moreover, through the sharing of local training data, the centralized learning approach has proven to improve a model's performance. In this research, a segmented federated learning is proposed, different from a collaborative learning based on single global model in a traditional federated learning model, it keeps multiple global models which allow each segment of participants to conduct collaborative learning separately and rearranges the segmentation of participants dynamically as well. Furthermore, these multiple global models interact with each other for updating parameters, thus being adaptable to various participants' LANs. A dataset covering two months' traffic data from 20 participants' LANs in the LAN-Security Monitoring Project is used. We adopt three types of knowledge-based methods for labeling network events and train a CNN model based on the dataset. At last, we achieve validation accuracies of 0.923, 0.813 and 0.877 individually with these labeling methods.

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yuweisunn/segmented-FL pytorch report

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Tasks

Federated Learning Intrusion Detection Network Intrusion Detection Personalized Federated Learning

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Network Intrusion Detection	SIDD-Image	Segmented-FL	F1 Score	0.893	#1 of 1	Archive leaderboard	report

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