FedSBS: Federated-Learning participant-selection method for Intrusion Detection Systems
Federated Learning (FL) is a decentralized machine learning approach in which multiple participants collaboratively train a model. Participants keep data locally, train their local models, and aggregate them in a single global model in a federated server. Collaborative FL-based Intrusion Detection S...
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Published in | Computer networks (Amsterdam, Netherlands : 1999) Vol. 244; p. 110351 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
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Elsevier B.V
01.05.2024
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Abstract | Federated Learning (FL) is a decentralized machine learning approach in which multiple participants collaboratively train a model. Participants keep data locally, train their local models, and aggregate them in a single global model in a federated server. Collaborative FL-based Intrusion Detection Systems face challenges on an uneven statistical distribution of data and malicious participants trying to subvert the learning process. The statistical hurdles associated with imbalanced data and malicious participants pose a risk of skewing the training with biased or random data. The inability to effectively manage these statistical inconsistencies may degrade system performance, leading to false intrusion detection or opening avenues for cybersecurity breaches. To overcome these challenges, we propose a training method that employs score-based participant selection and utilizes global momentum for model aggregation. Our method improves the global model performance while mitigating the risks posed by malicious participants. The proposal incorporates a scoring system based on an information gain variant to evaluate each participant’s contribution. The scoring system and an epsilon greedy selection method ensure robust participant selection in each aggregation round. Furthermore, incorporating a global momentum term helps preserve previous knowledge at each aggregation round, contributing to model stability and overall learning. The proposed solution has demonstrated superior performance, delivering 80% F1-Score and 90% accuracy on experiments even in the presence of malicious participants, revealing the robustness and effectiveness of the proposal in mitigating statistical challenges. Consequently, the proposed method significantly enhances the performance of federated learning models, leading to more secure and efficient collaborative intrusion detection systems. |
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AbstractList | Federated Learning (FL) is a decentralized machine learning approach in which multiple participants collaboratively train a model. Participants keep data locally, train their local models, and aggregate them in a single global model in a federated server. Collaborative FL-based Intrusion Detection Systems face challenges on an uneven statistical distribution of data and malicious participants trying to subvert the learning process. The statistical hurdles associated with imbalanced data and malicious participants pose a risk of skewing the training with biased or random data. The inability to effectively manage these statistical inconsistencies may degrade system performance, leading to false intrusion detection or opening avenues for cybersecurity breaches. To overcome these challenges, we propose a training method that employs score-based participant selection and utilizes global momentum for model aggregation. Our method improves the global model performance while mitigating the risks posed by malicious participants. The proposal incorporates a scoring system based on an information gain variant to evaluate each participant’s contribution. The scoring system and an epsilon greedy selection method ensure robust participant selection in each aggregation round. Furthermore, incorporating a global momentum term helps preserve previous knowledge at each aggregation round, contributing to model stability and overall learning. The proposed solution has demonstrated superior performance, delivering 80% F1-Score and 90% accuracy on experiments even in the presence of malicious participants, revealing the robustness and effectiveness of the proposal in mitigating statistical challenges. Consequently, the proposed method significantly enhances the performance of federated learning models, leading to more secure and efficient collaborative intrusion detection systems. |
ArticleNumber | 110351 |
Author | Hribar, Jernej Mattos, Diogo M.F. Cunha Neto, Helio N. Fernandes, Natalia C. Dusparic, Ivana |
Author_xml | – sequence: 1 givenname: Helio N. orcidid: 0000-0001-5072-8102 surname: Cunha Neto fullname: Cunha Neto, Helio N. email: heliocunha@midiacom.uff.br organization: Universidade Federal Fluminense, MidiaCom - PPGEET/UFF, Niterói, Rio de Janeiro, Brazil – sequence: 2 givenname: Jernej orcidid: 0000-0002-9446-7917 surname: Hribar fullname: Hribar, Jernej email: Jernej.Hribar@ijs.si organization: Jožef Stefan Institute, Department for Communication Systems, Slovenia – sequence: 3 givenname: Ivana surname: Dusparic fullname: Dusparic, Ivana email: Ivana.Dusparic@tcd.ie organization: Trinity College Dublin, Dublin, Ireland – sequence: 4 givenname: Natalia C. surname: Fernandes fullname: Fernandes, Natalia C. email: natalia@midiacom.uff.br organization: Universidade Federal Fluminense, MidiaCom - PPGEET/UFF, Niterói, Rio de Janeiro, Brazil – sequence: 5 givenname: Diogo M.F. surname: Mattos fullname: Mattos, Diogo M.F. email: menezes@midiacom.uff.br organization: Universidade Federal Fluminense, MidiaCom - PPGEET/UFF, Niterói, Rio de Janeiro, Brazil |
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Cites_doi | 10.1109/COMST.2020.2986024 10.1186/s42400-021-00105-6 10.1016/j.comcom.2022.09.012 10.1109/CVPR46437.2021.01057 10.1016/j.cose.2022.102675 10.1109/JIOT.2021.3079104 10.1109/TCSS.2023.3259431 10.1002/cpe.5344 10.1109/ICC.2019.8761315 10.1109/TPDS.2020.2975189 10.1016/j.future.2018.09.051 10.1145/3298981 10.1109/CCST.2019.8888419 10.1109/ACCESS.2023.3269980 10.1109/MSP.2020.2975749 |
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(IJITEE) contributor: fullname: Azhagusundari – start-page: 1227 year: 2019 ident: 10.1016/j.comnet.2024.110351_b15 article-title: Feddane: A federated newton-type method contributor: fullname: Li – volume: 37 start-page: 50 issue: 3 year: 2020 ident: 10.1016/j.comnet.2024.110351_b18 article-title: Federated learning: Challenges, methods, and future directions publication-title: IEEE Signal Process. Mag. doi: 10.1109/MSP.2020.2975749 contributor: fullname: Li |
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Title | FedSBS: Federated-Learning participant-selection method for Intrusion Detection Systems |
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