Developing a Transferable Federated Network Intrusion Detection System
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866908486748602368 |
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| author | Jameel, Abu Shafin Mohammad Mahdee Ghosh, Shreya Gamal, Aly El |
| author_facet | Jameel, Abu Shafin Mohammad Mahdee Ghosh, Shreya Gamal, Aly El |
| contents | Intrusion Detection Systems (IDS) are a vital part of a network-connected device. In this paper, we develop a deep learning based intrusion detection system that is deployed in a distributed setup across devices connected to a network. Our aim is to better equip deep learning models against unknown attacks using knowledge from known attacks. To this end, we develop algorithms to maximize the number of transferability relationships. We propose a Convolutional Neural Network (CNN) model, along with two algorithms that maximize the number of relationships observed. One is a two step data pre-processing stage, and the other is a Block-Based Smart Aggregation (BBSA) algorithm. The proposed system succeeds in achieving superior transferability performance while maintaining impressive local detection rates. We also show that our method is generalizable, exhibiting transferability potential across datasets and even with different backbones. The code for this work can be found at https://github.com/ghosh64/tabfidsv2. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_09060 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Developing a Transferable Federated Network Intrusion Detection System Jameel, Abu Shafin Mohammad Mahdee Ghosh, Shreya Gamal, Aly El Cryptography and Security Machine Learning Networking and Internet Architecture Signal Processing Intrusion Detection Systems (IDS) are a vital part of a network-connected device. In this paper, we develop a deep learning based intrusion detection system that is deployed in a distributed setup across devices connected to a network. Our aim is to better equip deep learning models against unknown attacks using knowledge from known attacks. To this end, we develop algorithms to maximize the number of transferability relationships. We propose a Convolutional Neural Network (CNN) model, along with two algorithms that maximize the number of relationships observed. One is a two step data pre-processing stage, and the other is a Block-Based Smart Aggregation (BBSA) algorithm. The proposed system succeeds in achieving superior transferability performance while maintaining impressive local detection rates. We also show that our method is generalizable, exhibiting transferability potential across datasets and even with different backbones. The code for this work can be found at https://github.com/ghosh64/tabfidsv2. |
| title | Developing a Transferable Federated Network Intrusion Detection System |
| topic | Cryptography and Security Machine Learning Networking and Internet Architecture Signal Processing |
| url | https://arxiv.org/abs/2508.09060 |