{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T00:43:05Z","timestamp":1769733785290,"version":"3.49.0"},"reference-count":37,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2022,10,12]],"date-time":"2022-10-12T00:00:00Z","timestamp":1665532800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012226","name":"the Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2021YJS008"],"award-info":[{"award-number":["2021YJS008"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"the Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2018YFA0701604"],"award-info":[{"award-number":["2018YFA0701604"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"the National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2021YJS008"],"award-info":[{"award-number":["2021YJS008"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"the National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2018YFA0701604"],"award-info":[{"award-number":["2018YFA0701604"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Aiming at the problems of single detection target of existing distributed denial of service (DDoS) attacks, incomplete detection datasets and privacy caused by shared datasets, we propose a trusted multi-domain DDoS detection method based on federated learning. Firstly, we divide the types of DDoS attacks into different sub-attacks, design the federated learning dataset for DDoS detection in each domain, and use them to realize a more comprehensive detection method of DDoS attacks on the premise of protecting the data privacy of each domain. Secondly, in order to improve the robustness of federated learning and alleviate poisoning attack, we propose a reputation evaluation method based on blockchain, which estimates interaction reputation, data reputation and resource reputation of each participant comprehensively, so as to obtain the trusted federated learning participants and identify the malicious participants. In addition, we also propose a combination scheme of multi-domain detection and distributed knowledge base and design a feature graph of malicious behavior based on a knowledge graph to realize the memory of multi-domain feature knowledge. The experimental results show that the accuracy of most categories of the multi-domain DDoS detection method can reach more than 95% with the protection of datasets, and the reputation evaluation method proposed in this paper has a higher ability to identify malicious participants against the data poisoning attack when the threshold is set to 0.6.<\/jats:p>","DOI":"10.3390\/s22207753","type":"journal-article","created":{"date-parts":[[2022,10,12]],"date-time":"2022-10-12T22:45:29Z","timestamp":1665614729000},"page":"7753","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Trusted Multi-Domain DDoS Detection Based on Federated Learning"],"prefix":"10.3390","volume":"22","author":[{"given":"Ziwei","family":"Yin","sequence":"first","affiliation":[{"name":"School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China"}]},{"given":"Kun","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China"}]},{"given":"Hongjun","family":"Bi","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.comcom.2020.05.043","article-title":"Learning to upgrade internet information security and protection strategy in big data era","volume":"160","author":"Guo","year":"2020","journal-title":"Comput. Commun."},{"key":"ref_2","unstructured":"CNCERT (2022, August 16). Analysis Report on DDOS Attack Resources in China. Available online: https:\/\/www.cert.org.cn\/publish\/main\/upload\/File\/DDos%20Attack%202021%20Q4.pdf."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Feng, B., Huang, Y., Tian, A., Wang, H., Zhou, H., Yu, S., and Zhang, H. (2022). An Elastic Differentiated Routing Framework for Software-Defined Satellite Networks. IEEE Wirel. Commun., 1\u20137.","DOI":"10.1109\/MWC.011.2100578"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2242","DOI":"10.1109\/COMST.2015.2457491","article-title":"Botnet in DDoS Attacks: Trends and Challenges","volume":"17","author":"Hoque","year":"2015","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Li, M., Zhou, H., and Qin, Y. (2022). Two-Stage Intelligent Model for Detecting Malicious DDoS Behavior. Sensors, 22.","DOI":"10.3390\/s22072532"},{"key":"ref_6","unstructured":"Li, Y., and Li, M. (2022). Multi-type application layer DDoS attack detection method based on integrated. J. Comput. Appl., 1\u20139. Available online: https:\/\/kns.cnki.net\/kcms\/detail\/51.1307.TP.20220416.0837.004.html."},{"key":"ref_7","first-page":"1","article-title":"Multi-class DRDoS Attack Detection Method Based on Feature Selection","volume":"7","author":"Yang","year":"2021","journal-title":"Res. Briefs Inf. Commun. Technol. Evol. (ReBICTE)"},{"key":"ref_8","first-page":"73","article-title":"Multi-type low-rate DDoS attack detection method based on hybrid deep learning","volume":"8","author":"Li","year":"2022","journal-title":"Chin. J. Netw. Inf. Secur."},{"key":"ref_9","first-page":"1845","article-title":"Online botnet detection method based on ensemble learning","volume":"39","author":"Shen","year":"2022","journal-title":"Appl. Res. Comput."},{"key":"ref_10","unstructured":"Mcmahan, H.B., Moore, E., and Ramage, D. (2016). Communication-Efficient Learning of Deep Networks from Decentralized Data. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Yang, M., He, Y., and Qiao, J. (2021, January 26\u201328). Federated Learning-Based Privacy-Preserving and Security: Survey. Proceedings of the 2021 Computing, Communications and IoT Applications (ComComAp), Shenzhen, China.","DOI":"10.1109\/ComComAp53641.2021.9653016"},{"key":"ref_12","unstructured":"Sullivan, J. (2022, August 16). Secure Analytics: Federated Learning and Secure Aggregation. Available online: http:\/\/www-inst.eecs.berkeley.edu\/~cs261\/fa18\/scribe\/10_15_revised.pdf."},{"key":"ref_13","unstructured":"Bhagoji, A.N., Chakraborty, S., and Mittal, P. (2019, January 9\u201315). Analyzing federated learning through an adversarial lens. Proceedings of the 36th International Conference on Machine Learning, Long Beach, CA, USA."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Priya, S., Sivaram, M., Yuvaraj, D., and Jayanthiladevi, A. (2020, January 12\u201314). Machine Learning based DDOS Detection. Proceedings of the 2020 International Conference on Emerging Smart Computing and Informatics (ESCI), Pune, India.","DOI":"10.1109\/ESCI48226.2020.9167642"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1109\/MNET.100.2000338","article-title":"Enabling Machine Learning with Service Function Chaining for Security Enhancement at 5G Edges","volume":"35","author":"Feng","year":"2021","journal-title":"IEEE Netw."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ashraf, A., and Elmedany, W.M. (2021, January 25\u201326). IoT DDoS attacks detection using machine learning techniques: A Review. Proceedings of the 2021 International Conference on Data Analytics for Business and Industry (ICDABI), Sakheer, Bahrain.","DOI":"10.1109\/ICDABI53623.2021.9655789"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"12931","DOI":"10.1109\/JIOT.2022.3163776","article-title":"Efficient Cache Consistency Management for Transient IoT Data in Content-Centric Networking","volume":"9","author":"Feng","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1109\/COMST.2018.2863942","article-title":"Security data collection and data analytics in the internet: A survey","volume":"21","author":"Jing","year":"2018","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3193","DOI":"10.1007\/s10489-018-1141-2","article-title":"Semi-supervised machine learning approach for DDoS detection","volume":"48","author":"Idhammad","year":"2018","journal-title":"Appl. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Mhamdi, L., McLernon, D., El-Moussa, F., Zaidi, S.A.R., Ghogho, M., and Tang, T. (2020, January 27\u201330). A Deep Learning Approach Combining Autoencoder with One-class SVM for DDoS Attack Detection in SDNs. Proceedings of the 2020 IEEE Eighth International Conference on Communications and Networking (ComNet), Hammamet, Tunisia.","DOI":"10.1109\/ComNet47917.2020.9306073"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"43920","DOI":"10.1109\/ACCESS.2020.2976609","article-title":"Low-Rate DoS Attacks, Detection, Defense, and Challenges: A Survey","volume":"8","author":"Zhijun","year":"2020","journal-title":"IEEE Access"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Tsiatsikas, Z., Geneiatakis, D., and Kambourakis, G. (2016). Realtime ddos detection in sip ecosystems: Machine learning tools of the trade. Network and System Security, Springer.","DOI":"10.1007\/978-3-319-46298-1_9"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Preuveneers, D., Rimmer, V., Tsingenopoulos, I., Spooren, J., Joosen, W., and Ilie-Zudor, E. (2018). Chained anomaly detection models for federated learning: An intrusion detection case study. Appl. Sci., 8.","DOI":"10.3390\/app8122663"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"214852","DOI":"10.1109\/ACCESS.2020.3041641","article-title":"Distributed Network Intrusion Detection System in Satellite-Terrestrial Integrated Networks Using Federated Learning","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Access"},{"key":"ref_25","unstructured":"Zhao, Y., Li, M., Lai, L., and Suda, N. (2018). Federated Learning with non-iid data. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"101157","DOI":"10.1016\/j.phycom.2020.101157","article-title":"Intelligent intrusion detection based on federated learning aided long short-term memory","volume":"42","author":"Zhao","year":"2020","journal-title":"Phys. Commun."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhang, J., Yu, P., Qi, L., Liu, S., Zhang, H., and Zhang, J. (2021, January 20\u201322). FLDDoS: DDoS Attack Detection Model based on Federated Learning. Proceedings of the 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), Shenyang, China.","DOI":"10.1109\/TrustCom53373.2021.00095"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Tian, Q., Guang, C., Wenchao, C., and Si, W. (2021, January 10\u201313). A Lightweight Residual Networks Framework for DDoS Attack Classification Based on Federated Learning. Proceedings of the IEEE INFOCOM 2021\u2014IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), online.","DOI":"10.1109\/INFOCOMWKSHPS51825.2021.9484622"},{"key":"ref_29","unstructured":"Yin, D., Chen, Y., Kannan, R., and Bartlett, P. (2018, January 10\u201315). Byzantine-robust distributed learning: Towards optimal statistical rates. Proceedings of the 35th International Conference on Machine Learning, Stockhome, Sweden."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Tahmasebian, F., Lou, J., and Xiong, L. (2021). Robustfed: A truth inference approach for robust federated learning. arXiv.","DOI":"10.1145\/3511808.3557439"},{"key":"ref_31","unstructured":"Park, J., Han, D.-J., and Choi, M. (2021). Sageflow: Robust federated learning against both stragglers and adversaries. Advances in Neural Information Processing Systems, MIT Press."},{"key":"ref_32","unstructured":"Xu, X., and Lyu, L. (2021, January 24). A reputation mechanism is all you need: Collaborative fairness and adversarial robustness in federated learning. Proceedings of the International Workshop on Federated Learning for User Privacy and Data Confidentiality in Conjunction with ICML(FL-ICML\u201921), online."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"10700","DOI":"10.1109\/JIOT.2019.2940820","article-title":"Incentive Mechanism for Reliable Federated Learning: A Joint Optimization Approach to Combining Reputation and Contract Theory","volume":"6","author":"Kang","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Ding, Q., Zhu, J., and Li, D. (2021, January 29). Blockchain Empowered Reliable Federated Learning by Worker Selection: A Trustworthy Reputation Evaluation Method. Proceedings of the 2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW), Nanjing, China.","DOI":"10.1109\/WCNCW49093.2021.9420026"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Uprety, A., and Rawat, D.B. (2021, January 5\u20137). Mitigating Poisoning Attack in Federated Learning. Proceedings of the 2021 IEEE Symposium Series on Computational Intelligence (SSCI), Orlando, FL, USA.","DOI":"10.1109\/SSCI50451.2021.9659839"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Mugunthan, V., Rahman, R., and Kagal, L. (2020). BlockFLow: An Accountable and Privacy-Preserving Solution for Federated Learning. arXiv.","DOI":"10.1145\/3340531.3412771"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1109\/MIS.2021.3082561","article-title":"SecureBoost: A Lossless Federated Learning Framework","volume":"36","author":"Cheng","year":"2021","journal-title":"IEEE Intell. 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