Federated learning for network attack detection using attention-based graph neural networks
Federated Learning is an effective solution to address the issues of data isolation and privacy leakage in machine learning. However, ensuring the security of network devices and architectures deploying federated learning remains a challenge due to network attacks. This paper proposes an attention-b...
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| Published in | Scientific reports Vol. 14; no. 1; pp. 19088 - 16 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
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London
Nature Publishing Group UK
17.08.2024
Nature Publishing Group Nature Portfolio |
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| Abstract | Federated Learning is an effective solution to address the issues of data isolation and privacy leakage in machine learning. However, ensuring the security of network devices and architectures deploying federated learning remains a challenge due to network attacks. This paper proposes an attention-based Graph Neural Network for detecting cross-level and cross-department network attacks. This method enables collaborative model training while protecting data privacy on distributed devices. By organizing network traffic information in chronological order and constructing a graph structure based on log density, enhances the accuracy of network attack detection. The introduction of an attention mechanism and the construction of a Federated Graph Attention Network (FedGAT) model are used to evaluate the interactivity between nodes in the graph, thereby improving the precision of internal network interactions. Experimental results demonstrate that our method achieves comparable accuracy and robustness to traditional detection methods while prioritizing privacy protection and data security. |
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| AbstractList | Federated Learning is an effective solution to address the issues of data isolation and privacy leakage in machine learning. However, ensuring the security of network devices and architectures deploying federated learning remains a challenge due to network attacks. This paper proposes an attention-based Graph Neural Network for detecting cross-level and cross-department network attacks. This method enables collaborative model training while protecting data privacy on distributed devices. By organizing network traffic information in chronological order and constructing a graph structure based on log density, enhances the accuracy of network attack detection. The introduction of an attention mechanism and the construction of a Federated Graph Attention Network (FedGAT) model are used to evaluate the interactivity between nodes in the graph, thereby improving the precision of internal network interactions. Experimental results demonstrate that our method achieves comparable accuracy and robustness to traditional detection methods while prioritizing privacy protection and data security.Federated Learning is an effective solution to address the issues of data isolation and privacy leakage in machine learning. However, ensuring the security of network devices and architectures deploying federated learning remains a challenge due to network attacks. This paper proposes an attention-based Graph Neural Network for detecting cross-level and cross-department network attacks. This method enables collaborative model training while protecting data privacy on distributed devices. By organizing network traffic information in chronological order and constructing a graph structure based on log density, enhances the accuracy of network attack detection. The introduction of an attention mechanism and the construction of a Federated Graph Attention Network (FedGAT) model are used to evaluate the interactivity between nodes in the graph, thereby improving the precision of internal network interactions. Experimental results demonstrate that our method achieves comparable accuracy and robustness to traditional detection methods while prioritizing privacy protection and data security. Federated Learning is an effective solution to address the issues of data isolation and privacy leakage in machine learning. However, ensuring the security of network devices and architectures deploying federated learning remains a challenge due to network attacks. This paper proposes an attention-based Graph Neural Network for detecting cross-level and cross-department network attacks. This method enables collaborative model training while protecting data privacy on distributed devices. By organizing network traffic information in chronological order and constructing a graph structure based on log density, enhances the accuracy of network attack detection. The introduction of an attention mechanism and the construction of a Federated Graph Attention Network (FedGAT) model are used to evaluate the interactivity between nodes in the graph, thereby improving the precision of internal network interactions. Experimental results demonstrate that our method achieves comparable accuracy and robustness to traditional detection methods while prioritizing privacy protection and data security. Abstract Federated Learning is an effective solution to address the issues of data isolation and privacy leakage in machine learning. However, ensuring the security of network devices and architectures deploying federated learning remains a challenge due to network attacks. This paper proposes an attention-based Graph Neural Network for detecting cross-level and cross-department network attacks. This method enables collaborative model training while protecting data privacy on distributed devices. By organizing network traffic information in chronological order and constructing a graph structure based on log density, enhances the accuracy of network attack detection. The introduction of an attention mechanism and the construction of a Federated Graph Attention Network (FedGAT) model are used to evaluate the interactivity between nodes in the graph, thereby improving the precision of internal network interactions. Experimental results demonstrate that our method achieves comparable accuracy and robustness to traditional detection methods while prioritizing privacy protection and data security. |
| ArticleNumber | 19088 |
| Author | Jianping, Wu Guangqiu, Qiu Weiwei, Jiang Chunming, Wu Jiahe, Jin |
| Author_xml | – sequence: 1 givenname: Wu surname: Jianping fullname: Jianping, Wu organization: College of Computer Science and Technology, Zhejiang University – sequence: 2 givenname: Qiu surname: Guangqiu fullname: Guangqiu, Qiu organization: Smart Government R &D Center (Laboratory) of Hangzhou Dianzi University – sequence: 3 givenname: Wu surname: Chunming fullname: Chunming, Wu email: wuchunming@zju.edu.cn organization: College of Computer Science and Technology, Zhejiang University – sequence: 4 givenname: Jiang surname: Weiwei fullname: Weiwei, Jiang organization: Key Laboratory of Universal Wireless Communications, Ministry of Education, Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, Anhui University, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications – sequence: 5 givenname: Jin surname: Jiahe fullname: Jiahe, Jin organization: Key Laboratory of Key Technologies for Open Data Fusion in Zhejiang Province |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39154072$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.48550/arXiv.1806.02371 10.1109/EIDWT.2012.10 10.48550/arXiv.1810.10751 10.1109/OJCOMS.2020.3044323 10.1016/j.ress.2024.109950 10.1007/s11277-017-4909-5 10.1038/s41467-022-30714-9 10.1504/IJBIDM.2019.099963 10.1016/j.comnet.2019.02.019 10.1007/s11276-015-1065-2 10.1109/ACCESS.2019.2925828 10.11959/j.issn.2096-109x.2018056 10.1109/TrustCom60117.2023.00043 10.3390/axioms12020170 10.1145/3368926.3369705 10.48550/arXiv.2401.13421 10.48550/arXiv.1602.05629 10.3837/tiis.2018.10.028 10.1007/s10586-018-2385-7 10.1016/j.knosys.2024.111876 10.48550/arXiv.1710.10903 10.1155/2021/9361348 10.48550/arXiv.2207.07444 10.48550/arXiv.2405.03644 10.48550/arXiv.2202.07256 10.3390/s24113481 10.1109/ACCESS.2019.2907965 10.1007/BF02777627 10.1109/IWCMC.2019.8766592 10.1109/MSEC.2019.2939728 10.1016/j.knosys.2024.112029 10.1109/TII.2020.3023430 10.1016/j.phycom.2020.101157 10.1109/IAdCC.2014.6779523 10.1109/MSEC.2020.3037845 10.48550/arXiv.2403.02240 10.1145/3653448 10.1109/TIFS.2018.2812149 10.1016/j.comcom.2022.09.012 10.48550/arXiv.2111.02791 10.1109/TSE.1987.232894 10.1016/j.seta.2022.102987 10.1109/tdsc.2014.2312327 10.1002/spe.3039 10.1109/COMST.2019.2896380 10.1109/TNN.2008.2005605 10.1109/CSCN.2019.8931384 10.1109/ACCESS.2019.2904620 |
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| References | ScarselliFThe graph neural network modelIEEE Trans. Neural Networks200920618010.1109/TNN.2008.200560519068426 Daskin, A. Federated learning with distributed fixed design quantum chips and quantum channels. arxiv:abs/2401.13421. https://doi.org/10.48550/arXiv.2401.13421(2024). Sarhan, M. et al Cyber threat intelligence sharing scheme based on federated learning for network intrusion detection. J. Netw. Syst. Manag. 31,1–23. https://doi.org/10.48550/arXiv.2111.02791 (2021). Zhang, Y. et al Federated learning with quantum secure aggregation. arxiv:abs/2207.07444. https://doi.org/10.48550/arXiv.2207.07444(2023). ManDIntelligent intrusion detection based on federated learning for edge-assisted internet of thingsSecur. Commun. Netw.20212021936134810.1155/2021/9361348 Nabavirazavi, S. et al Impact of aggregation function randomization against model poisoning in federated learning. In 2023 IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). 165–172. https://doi.org/10.1109/TrustCom60117.2023.00043(2023). WangXFedKG: A knowledge distillation-based federated graph method for social bot detectionSensors202424348110.3390/s241134813889427611174749 Gill, S. S. et al Quantum computing: Vision and challenges. arxiv:abs/2403.02240. https://doi.org/10.48550/arXiv.2403.02240(2024). MitchellRChenIBehavior rule specification-based intrusion detection for safety critical medical cyber physical systemsIEEE Trans. Dependable Secure Comput.201512163010.1109/tdsc.2014.2312327 Neisse, R. et al Toward a blockchain-based platform to manage cybersecurity certification of iot devices. 2019 IEEE Conference on Standards for Communications and Networking (CSCN).1–6. https://doi.org/10.1109/CSCN.2019.8931384(2019). YanBHanGCombinatorial intrusion detection model based on deep recurrent neural network and improved smote algorithmChin. J. Netw. Inf. Secur.20184485910.11959/j.issn.2096-109x.2018056 XiaoYXingCZhangTZhaoZAn intrusion detection model based on feature reduction and convolutional neural networksIEEE Access20197422104221910.1109/ACCESS.2019.2904620 DenningDEAn intrusion-detection modelIEEE Trans. Softw. Eng.19871322223210.1109/TSE.1987.232894 Mohamed, M. B. et al. Intrusion cancellation for anomaly detection in healthcare applications. In 2019 15th International Wireless Communications and Mobile Computing Conference (IWCMC). 313-318(2019). ZhaoRIntelligent intrusion detection based on federated learning aided long short-termmemoryPhys. Commun.20204210.1016/j.phycom.2020.101157 ZhangJSMulti-hop graph pooling adversarial network for cross-domain remaining useful life prediction: A distributed federated learning perspectiveReliab. Eng. Syst. Saf.202422410.1016/j.ress.2024.109950 MaSLiuJWReview of graph neural networksChin. J. Comput. Res. Dev.2022594780 Hernandez-Ramos, J. L., Matheu, S. N. & Skarmeta, A. The challenges of software cybersecurity certification. IEEE Secur. Priv. Mag. 19, 99-102. https://doi.org/10.1109/MSEC.2020.3037845(2021) Checkoway, S. et al. Comprehensive experimental analyses of automotive attack surfaces. Proceedings of the 20th USENIX Conference on Security. 1–6. https://doi.org/10.1007/BF02777627 (2011). Al-HuthaifiRFedGODE: Secure traffic flow prediction based on federated learning and graph ordinary differential equation networksKnowl.-Based Syst.202429910.1016/j.knosys.2024.112029 TianCXieYChenXLiYZhaoXPrivacy-preserving cross-domain recommendation with federated graph learningACM Trans. Inf. Syst.20244212910.1145/3653448 ChuangMfedgr: Federated graph neural network for recommendation systemsAXIOMS20231217010.3390/axioms12020170 Wang, X. et al Attack graph convolutional networks by adding fake nodes. arXiv:1810.10751. https://doi.org/10.48550/arXiv.1810.10751 (2018). AgrawalSReview federated learning for intrusion detection system: Concepts, challenges and future directionsComput. Commun.202219534636110.1016/j.comcom.2022.09.012 WeiPLiYZhangZLiuDAn optimization method for intrusion detection classification model based on deep belief networkIEEE Access20197875938760510.1109/ACCESS.2019.2925828 Zhang, J. et al When llms meet cybersecurity: A systematic literature review. arxiv:abs/2405.03644. https://doi.org/10.48550/arXiv.2405.03644 (2024). Mcmahan, H. B. et al Communication-efficient learning of deep networks from decentralized data. The 20th International Conference on Artificial Intelligence and Statistics (AISTATS). 54, 1273–1282. https://doi.org/10.48550/arXiv.1602.05629(2016). Zhao, Y. et al Multi-task network anomaly detection using federated learning. In the Tenth International Symposium. 273–279. https://doi.org/10.1145/3368926.3369705(2019). PandyaSFederated learning for smart cities: A comprehensive surveySustain. Eenergy Technol. Assessments20235510.1016/j.seta.2022.102987 Velikovi, P. et al. Graph attention networks. ArXiv. abs/1710.10903. https://doi.org/10.48550/arXiv.1710.10903(2017). ChaabouniNNetwork intrusion detection for iot security based on learning techniquesIEEE Commun. Surv. Tutor.2019212671270110.1109/COMST.2019.2896380 WuCA federated graph neural network framework for privacy-preserving personalizationNat. Commun.20221330912022NatCo..13.3091W10.1038/s41467-022-30714-93565479291631031:CAS:528:DC%2BB38XhsVOqtrjK KongXMitigating data imbalance and generating better prototypes in heterogeneous federated graph learningKnowl.-Based Syst.202429610.1016/j.knosys.2024.111876 GaoLIntrusion detection system using soeks and deep learning for in-vehicle securityClust. Comput.201922147211472910.1007/s10586-018-2385-7 JanSToward a lightweight intrusion detection system for the internet of thingsIEEE Access.20197424504247110.1109/ACCESS.2019.2907965 Zhou, K. et al Attacking similarity-based link prediction in social networks. In International Conference on Autonomous Agents and Multiagent Systems. 305–313(2019). LiBDeepfed: Federated deep learning for intrusion detection in industrial cyber-physical systemsIEEE Trans. Ind. Inf.2020175615562410.1109/TII.2020.3023430 Hernandez-RamosJLToward a data-driven society: A technological perspective on the development of cybersecurity and data protection policiesIEEE Secur. Priv. Mag.202018283810.1109/MSEC.2019.2939728 NaseerSSaleemYEnhanced network intrusion detection using deep convolutional neural networksKSII Trans. Internet Inf. Syst.2018125159517810.3837/tiis.2018.10.028 ChoiWVoltageids: Low-level communication characteristics for automotive intrusion detection systemIEEE Trans. Inf. For. Secur.2018132114212910.1109/TIFS.2018.2812149 GhoshKNeogySDasPKMehtaMIntrusion detection at international borders and large military barracks with multi-sink wireless sensor networks: An energy efficient solutionWirel. Pers. Commun.20179811910.1007/s11277-017-4909-5 Sahu, S. K. et al A detail analysis on intrusion detection datasets. 2014 IEEE International Advance Computing Conference (IACC). 1348–1353. https://doi.org/10.1109/IAdCC.2014.6779523(2014). Liu, R. et al Federated graph neural networks: Overview, techniques and challenges. ArXiv. abs/2202.07256. https://doi.org/10.48550/arXiv.2202.07256(2022). ManickamMRamarajNChellappanCA combined pfcm and recurrent neural network-based intrusion detection system for cloud environmentInt. J. Bus. Intell. Data Min.20191450452710.1504/IJBIDM.2019.099963 HalderSGhosalAContiMEfficient physical intrusion detection in internet of things: A node deployment approachComput. Networks.2019154284610.1016/j.comnet.2019.02.019 GillSSQuantum computing: A taxonomy, systematic review and future directionsSoftw. Pract. Exp.2022526611410.1002/spe.3039 ArthurMPKannanKCross-layer based multiclass intrusion detection system for secure multicast communication of manet in military networksWirel. Netw.2016221035105910.1007/s11276-015-1065-2 Dai, H. et al. Adversarial attack on graph structured data. arXiv:1806.02371. https://doi.org/10.48550/arXiv.1806.02371 (2018). Zargari, S. & Voorhis, D. Feature Selection in the Corrected KDD-dataset. Third International Conference on Emerging Intelligent Data & Web Technologies. 174–180. https://doi.org/10.1109/EIDWT.2012.10 (2012) SunYEsakiHOchiaiHAdaptive intrusion detection in the networking of large-scale lans with segmented federated learningIEEE Open J. Commun. 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| References_xml | – reference: WeiPLiYZhangZLiuDAn optimization method for intrusion detection classification model based on deep belief networkIEEE Access20197875938760510.1109/ACCESS.2019.2925828 – reference: KongXMitigating data imbalance and generating better prototypes in heterogeneous federated graph learningKnowl.-Based Syst.202429610.1016/j.knosys.2024.111876 – reference: Zargari, S. & Voorhis, D. Feature Selection in the Corrected KDD-dataset. Third International Conference on Emerging Intelligent Data & Web Technologies. 174–180. https://doi.org/10.1109/EIDWT.2012.10 (2012) – reference: MitchellRChenIBehavior rule specification-based intrusion detection for safety critical medical cyber physical systemsIEEE Trans. Dependable Secure Comput.201512163010.1109/tdsc.2014.2312327 – reference: ZhaoRIntelligent intrusion detection based on federated learning aided long short-termmemoryPhys. Commun.20204210.1016/j.phycom.2020.101157 – reference: WuCA federated graph neural network framework for privacy-preserving personalizationNat. Commun.20221330912022NatCo..13.3091W10.1038/s41467-022-30714-93565479291631031:CAS:528:DC%2BB38XhsVOqtrjK – reference: DenningDEAn intrusion-detection modelIEEE Trans. Softw. Eng.19871322223210.1109/TSE.1987.232894 – reference: Liu, R. et al Federated graph neural networks: Overview, techniques and challenges. ArXiv. abs/2202.07256. https://doi.org/10.48550/arXiv.2202.07256(2022). – reference: ChoiWVoltageids: Low-level communication characteristics for automotive intrusion detection systemIEEE Trans. Inf. For. Secur.2018132114212910.1109/TIFS.2018.2812149 – reference: GillSSQuantum computing: A taxonomy, systematic review and future directionsSoftw. Pract. Exp.2022526611410.1002/spe.3039 – reference: ZhangJSMulti-hop graph pooling adversarial network for cross-domain remaining useful life prediction: A distributed federated learning perspectiveReliab. Eng. Syst. Saf.202422410.1016/j.ress.2024.109950 – reference: Al-HuthaifiRFedGODE: Secure traffic flow prediction based on federated learning and graph ordinary differential equation networksKnowl.-Based Syst.202429910.1016/j.knosys.2024.112029 – reference: XiaoYXingCZhangTZhaoZAn intrusion detection model based on feature reduction and convolutional neural networksIEEE Access20197422104221910.1109/ACCESS.2019.2904620 – reference: Velikovi, P. et al. Graph attention networks. ArXiv. abs/1710.10903. https://doi.org/10.48550/arXiv.1710.10903(2017). – reference: Zhou, K. et al Attacking similarity-based link prediction in social networks. In International Conference on Autonomous Agents and Multiagent Systems. 305–313(2019). – reference: Sahu, S. K. et al A detail analysis on intrusion detection datasets. 2014 IEEE International Advance Computing Conference (IACC). 1348–1353. https://doi.org/10.1109/IAdCC.2014.6779523(2014). – reference: Sarhan, M. et al Cyber threat intelligence sharing scheme based on federated learning for network intrusion detection. J. Netw. Syst. Manag. 31,1–23. https://doi.org/10.48550/arXiv.2111.02791 (2021). – reference: Daskin, A. Federated learning with distributed fixed design quantum chips and quantum channels. arxiv:abs/2401.13421. https://doi.org/10.48550/arXiv.2401.13421(2024). – reference: ChaabouniNNetwork intrusion detection for iot security based on learning techniquesIEEE Commun. Surv. Tutor.2019212671270110.1109/COMST.2019.2896380 – reference: YanBHanGCombinatorial intrusion detection model based on deep recurrent neural network and improved smote algorithmChin. J. Netw. Inf. Secur.20184485910.11959/j.issn.2096-109x.2018056 – reference: ManDIntelligent intrusion detection based on federated learning for edge-assisted internet of thingsSecur. Commun. Netw.20212021936134810.1155/2021/9361348 – reference: Zhang, Y. et al Federated learning with quantum secure aggregation. arxiv:abs/2207.07444. https://doi.org/10.48550/arXiv.2207.07444(2023). – reference: HalderSGhosalAContiMEfficient physical intrusion detection in internet of things: A node deployment approachComput. Networks.2019154284610.1016/j.comnet.2019.02.019 – reference: Neisse, R. et al Toward a blockchain-based platform to manage cybersecurity certification of iot devices. 2019 IEEE Conference on Standards for Communications and Networking (CSCN).1–6. https://doi.org/10.1109/CSCN.2019.8931384(2019). – reference: PandyaSFederated learning for smart cities: A comprehensive surveySustain. Eenergy Technol. Assessments20235510.1016/j.seta.2022.102987 – reference: Checkoway, S. et al. Comprehensive experimental analyses of automotive attack surfaces. Proceedings of the 20th USENIX Conference on Security. 1–6. https://doi.org/10.1007/BF02777627 (2011). – reference: NaseerSSaleemYEnhanced network intrusion detection using deep convolutional neural networksKSII Trans. Internet Inf. Syst.2018125159517810.3837/tiis.2018.10.028 – reference: Wang, X. et al Attack graph convolutional networks by adding fake nodes. arXiv:1810.10751. https://doi.org/10.48550/arXiv.1810.10751 (2018). – reference: Mcmahan, H. B. et al Communication-efficient learning of deep networks from decentralized data. The 20th International Conference on Artificial Intelligence and Statistics (AISTATS). 54, 1273–1282. https://doi.org/10.48550/arXiv.1602.05629(2016). – reference: GaoLIntrusion detection system using soeks and deep learning for in-vehicle securityClust. Comput.201922147211472910.1007/s10586-018-2385-7 – reference: Hernandez-Ramos, J. L., Matheu, S. N. & Skarmeta, A. The challenges of software cybersecurity certification. IEEE Secur. Priv. Mag. 19, 99-102. https://doi.org/10.1109/MSEC.2020.3037845(2021) – reference: TianCXieYChenXLiYZhaoXPrivacy-preserving cross-domain recommendation with federated graph learningACM Trans. Inf. Syst.20244212910.1145/3653448 – reference: Dai, H. et al. Adversarial attack on graph structured data. arXiv:1806.02371. https://doi.org/10.48550/arXiv.1806.02371 (2018). – reference: Hernandez-RamosJLToward a data-driven society: A technological perspective on the development of cybersecurity and data protection policiesIEEE Secur. Priv. Mag.202018283810.1109/MSEC.2019.2939728 – reference: ManickamMRamarajNChellappanCA combined pfcm and recurrent neural network-based intrusion detection system for cloud environmentInt. J. Bus. Intell. Data Min.20191450452710.1504/IJBIDM.2019.099963 – reference: Mohamed, M. B. et al. Intrusion cancellation for anomaly detection in healthcare applications. In 2019 15th International Wireless Communications and Mobile Computing Conference (IWCMC). 313-318(2019). – reference: Nabavirazavi, S. et al Impact of aggregation function randomization against model poisoning in federated learning. In 2023 IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). 165–172. https://doi.org/10.1109/TrustCom60117.2023.00043(2023). – reference: LiBDeepfed: Federated deep learning for intrusion detection in industrial cyber-physical systemsIEEE Trans. Ind. Inf.2020175615562410.1109/TII.2020.3023430 – reference: ScarselliFThe graph neural network modelIEEE Trans. Neural Networks200920618010.1109/TNN.2008.200560519068426 – reference: JanSToward a lightweight intrusion detection system for the internet of thingsIEEE Access.20197424504247110.1109/ACCESS.2019.2907965 – reference: Zhang, J. et al When llms meet cybersecurity: A systematic literature review. arxiv:abs/2405.03644. https://doi.org/10.48550/arXiv.2405.03644 (2024). – reference: ChuangMfedgr: Federated graph neural network for recommendation systemsAXIOMS20231217010.3390/axioms12020170 – reference: WangXFedKG: A knowledge distillation-based federated graph method for social bot detectionSensors202424348110.3390/s241134813889427611174749 – reference: Gill, S. S. et al Quantum computing: Vision and challenges. arxiv:abs/2403.02240. https://doi.org/10.48550/arXiv.2403.02240(2024). – reference: MaSLiuJWReview of graph neural networksChin. J. Comput. Res. Dev.2022594780 – reference: GhoshKNeogySDasPKMehtaMIntrusion detection at international borders and large military barracks with multi-sink wireless sensor networks: An energy efficient solutionWirel. Pers. Commun.20179811910.1007/s11277-017-4909-5 – reference: AgrawalSReview federated learning for intrusion detection system: Concepts, challenges and future directionsComput. Commun.202219534636110.1016/j.comcom.2022.09.012 – reference: SunYEsakiHOchiaiHAdaptive intrusion detection in the networking of large-scale lans with segmented federated learningIEEE Open J. Commun. Soc.2020210211210.1109/OJCOMS.2020.3044323 – reference: ArthurMPKannanKCross-layer based multiclass intrusion detection system for secure multicast communication of manet in military networksWirel. Netw.2016221035105910.1007/s11276-015-1065-2 – reference: Zhao, Y. et al Multi-task network anomaly detection using federated learning. In the Tenth International Symposium. 273–279. https://doi.org/10.1145/3368926.3369705(2019). – ident: 70032_CR24 doi: 10.48550/arXiv.1806.02371 – ident: 70032_CR44 doi: 10.1109/EIDWT.2012.10 – ident: 70032_CR25 doi: 10.48550/arXiv.1810.10751 – volume: 2 start-page: 102 year: 2020 ident: 70032_CR35 publication-title: IEEE Open J. Commun. Soc. doi: 10.1109/OJCOMS.2020.3044323 – volume: 224 year: 2024 ident: 70032_CR4 publication-title: Reliab. Eng. Syst. Saf. doi: 10.1016/j.ress.2024.109950 – volume: 98 start-page: 1 year: 2017 ident: 70032_CR13 publication-title: Wirel. Pers. Commun. doi: 10.1007/s11277-017-4909-5 – volume: 13 start-page: 3091 year: 2022 ident: 70032_CR39 publication-title: Nat. Commun. doi: 10.1038/s41467-022-30714-9 – volume: 14 start-page: 504 year: 2019 ident: 70032_CR9 publication-title: Int. J. Bus. Intell. Data Min. doi: 10.1504/IJBIDM.2019.099963 – volume: 154 start-page: 28 year: 2019 ident: 70032_CR20 publication-title: Comput. Networks. doi: 10.1016/j.comnet.2019.02.019 – volume: 22 start-page: 1035 year: 2016 ident: 70032_CR14 publication-title: Wirel. Netw. doi: 10.1007/s11276-015-1065-2 – volume: 7 start-page: 87593 year: 2019 ident: 70032_CR8 publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2925828 – volume: 4 start-page: 48 year: 2018 ident: 70032_CR10 publication-title: Chin. J. Netw. Inf. Secur. doi: 10.11959/j.issn.2096-109x.2018056 – ident: 70032_CR27 doi: 10.1109/TrustCom60117.2023.00043 – volume: 12 start-page: 170 year: 2023 ident: 70032_CR5 publication-title: AXIOMS doi: 10.3390/axioms12020170 – ident: 70032_CR33 doi: 10.1145/3368926.3369705 – ident: 70032_CR48 doi: 10.48550/arXiv.2401.13421 – ident: 70032_CR1 doi: 10.48550/arXiv.1602.05629 – volume: 12 start-page: 5159 year: 2018 ident: 70032_CR7 publication-title: KSII Trans. Internet Inf. Syst. doi: 10.3837/tiis.2018.10.028 – volume: 22 start-page: 14721 year: 2019 ident: 70032_CR19 publication-title: Clust. Comput. doi: 10.1007/s10586-018-2385-7 – volume: 59 start-page: 47 year: 2022 ident: 70032_CR23 publication-title: Chin. J. Comput. Res. Dev. – ident: 70032_CR26 – volume: 296 year: 2024 ident: 70032_CR41 publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2024.111876 – ident: 70032_CR12 doi: 10.48550/arXiv.1710.10903 – volume: 2021 start-page: 9361348 year: 2021 ident: 70032_CR37 publication-title: Secur. Commun. Netw. doi: 10.1155/2021/9361348 – ident: 70032_CR49 doi: 10.48550/arXiv.2207.07444 – ident: 70032_CR46 doi: 10.48550/arXiv.2405.03644 – ident: 70032_CR38 doi: 10.48550/arXiv.2202.07256 – volume: 24 start-page: 3481 year: 2024 ident: 70032_CR43 publication-title: Sensors doi: 10.3390/s24113481 – volume: 7 start-page: 42450 year: 2019 ident: 70032_CR22 publication-title: IEEE Access. doi: 10.1109/ACCESS.2019.2907965 – ident: 70032_CR18 doi: 10.1007/BF02777627 – ident: 70032_CR16 doi: 10.1109/IWCMC.2019.8766592 – volume: 18 start-page: 28 year: 2020 ident: 70032_CR30 publication-title: IEEE Secur. Priv. Mag. doi: 10.1109/MSEC.2019.2939728 – volume: 299 year: 2024 ident: 70032_CR42 publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2024.112029 – volume: 17 start-page: 5615 year: 2020 ident: 70032_CR34 publication-title: IEEE Trans. Ind. Inf. doi: 10.1109/TII.2020.3023430 – volume: 42 year: 2020 ident: 70032_CR36 publication-title: Phys. Commun. doi: 10.1016/j.phycom.2020.101157 – ident: 70032_CR45 doi: 10.1109/IAdCC.2014.6779523 – ident: 70032_CR31 doi: 10.1109/MSEC.2020.3037845 – ident: 70032_CR47 doi: 10.48550/arXiv.2403.02240 – volume: 42 start-page: 1 year: 2024 ident: 70032_CR40 publication-title: ACM Trans. Inf. Syst. doi: 10.1145/3653448 – volume: 13 start-page: 2114 year: 2018 ident: 70032_CR17 publication-title: IEEE Trans. Inf. For. Secur. doi: 10.1109/TIFS.2018.2812149 – volume: 195 start-page: 346 year: 2022 ident: 70032_CR28 publication-title: Comput. Commun. doi: 10.1016/j.comcom.2022.09.012 – ident: 70032_CR29 doi: 10.48550/arXiv.2111.02791 – volume: 13 start-page: 222 year: 1987 ident: 70032_CR3 publication-title: IEEE Trans. Softw. Eng. doi: 10.1109/TSE.1987.232894 – volume: 55 year: 2023 ident: 70032_CR2 publication-title: Sustain. Eenergy Technol. Assessments doi: 10.1016/j.seta.2022.102987 – volume: 12 start-page: 16 year: 2015 ident: 70032_CR15 publication-title: IEEE Trans. Dependable Secure Comput. doi: 10.1109/tdsc.2014.2312327 – volume: 52 start-page: 66 year: 2022 ident: 70032_CR50 publication-title: Softw. Pract. Exp. doi: 10.1002/spe.3039 – volume: 21 start-page: 2671 year: 2019 ident: 70032_CR21 publication-title: IEEE Commun. Surv. Tutor. doi: 10.1109/COMST.2019.2896380 – volume: 20 start-page: 61 year: 2009 ident: 70032_CR11 publication-title: IEEE Trans. Neural Networks doi: 10.1109/TNN.2008.2005605 – ident: 70032_CR32 doi: 10.1109/CSCN.2019.8931384 – volume: 7 start-page: 42210 year: 2019 ident: 70032_CR6 publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2904620 |
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| Title | Federated learning for network attack detection using attention-based graph neural networks |
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