{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T10:20:24Z","timestamp":1777890024872,"version":"3.51.4"},"reference-count":16,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T00:00:00Z","timestamp":1638316800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Web Intelligence"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:p>With the rapid development of social networks and the massive popularity of\n                    intelligent mobile terminals, network anomaly detection is becoming increasingly\n                    important. In daily work and life, edge nodes store a large number of network\n                    local connection data and audit data, which can be used to analyze network\n                    abnormal behavior. With the increasingly close network communication, the amount\n                    of network connection and other related data collected by each network terminal\n                    is increasing. Machine learning has become a classification method to analyze\n                    the features of big data in the network. Face to the problems of excessive data\n                    and long response time for network anomaly detection, we propose a trust-based\n                    Federated learning anomaly detection algorithm. We use the edge nodes to train\n                    the local data model, and upload the machine learning parameters to the central\n                    node. Meanwhile, according to the performance of edge nodes training, we set\n                    different weights to match the processing capacity of each terminal which will\n                    obtain faster convergence speed and better attack classification accuracy. The\n                    user\u2019s private information will only be processed locally and will not be\n                    uploaded to the central server, which can reduce the risk of information\n                    disclosure. Finally, we compare the basic federated learning model and TFCNN\n                    algorithm on KDD Cup 99 dataset and MNIST dataset. The experimental results show\n                    that the TFCNN algorithm can improve accuracy and communication efficiency.<\/jats:p>","DOI":"10.3233\/web-210475","type":"journal-article","created":{"date-parts":[[2021,12,24]],"date-time":"2021-12-24T10:36:34Z","timestamp":1640342194000},"page":"317-327","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["Trust-based federated learning for network anomaly                    detection"],"prefix":"10.1177","volume":"19","author":[{"given":"Naiyue","family":"Chen","sequence":"first","affiliation":[{"name":"Beijing Key Laboratory of Security and Privacy in\r                    Intelligent Transportation, School of Computer and Information Technology,\r                        Beijing Jiaotong University, Beijing 100044,\r                        China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology,\r                        Beijing Jiaotong University, Beijing 100044,\r                        China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yinglong","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering,\r                        Beijing Jiaotong University, Beijing 100044,\r                        China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luxin","family":"Cai","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Security and Privacy in\r                    Intelligent Transportation, School of Computer and Information Technology,\r                        Beijing Jiaotong University, Beijing 100044,\r                        China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2021,12]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"crossref","unstructured":"K.\u00a0Bonawitz V.\u00a0Ivanov B.\u00a0Kreuter A.\u00a0Marcedone H.B.\u00a0McMahan S.\u00a0Patel D.\u00a0Ramage A.\u00a0Segal and K.\u00a0Seth Practical secure aggregation for privacy-preserving machine learning in: Conference on Computer and Communications Security (CCS) 2017 pp.\u00a01175\u20131191.","DOI":"10.1145\/3133956.3133982"},{"key":"e_1_3_1_3_2","doi-asserted-by":"crossref","unstructured":"N.\u00a0Dryden T.\u00a0Moon S.A.\u00a0Jacobs et al. Communication Quantization for Data-Parallel Training of Deep Neural Networks 2016 2nd Workshop on Machine Learning in HPC Environments (MLHPC) IEEE Press 2016.","DOI":"10.1109\/MLHPC.2016.004"},{"key":"e_1_3_1_4_2","unstructured":"R.\u00a0Ito M.\u00a0Tsukada and M.H.\u00a0An On-Device Federated Learning Approach for Cooperative Model Update between Edge Device 2020 2002.12301."},{"key":"e_1_3_1_5_2","unstructured":"J.\u00a0Konen H.B.\u00a0Mcmahan and D.\u00a0Ramage Federated optimization: Distributed machine learning for on-device intelligence 2016 1610.02527."},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2017.2777990"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2020.3018259"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3039828"},{"key":"e_1_3_1_9_2","unstructured":"B.\u00a0McMahan E.\u00a0Moore D.\u00a0Ramage S.\u00a0Hampson and B.A.\u00a0y\u00a0Arcas Communication-efficient learning of deep networks from decentralized data in: Artificial Intelligence and Statistics PMLR 2017 pp.\u00a01273\u20131282."},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.2971981"},{"key":"e_1_3_1_11_2","doi-asserted-by":"crossref","unstructured":"G.\u00a0Pang K.\u00a0Ming Ting and D.\u00a0Albrecht LeSiNN: Detecting anomalies by identifying least similar nearest neighbours in: ICDM Workshop 2015 pp.\u00a0623\u2013630.","DOI":"10.1109\/ICDMW.2015.62"},{"key":"e_1_3_1_12_2","unstructured":"G.\u00a0Pang C.\u00a0Shen H.\u00a0Jin and A.\u00a0van den Hengel Deep weakly-supervised anomaly detection 2019 1910.13601."},{"key":"e_1_3_1_13_2","doi-asserted-by":"crossref","unstructured":"N.\u00a0Strom Scalable Distributed DNN Training Using Commodity GPU Cloud Computing Sixteenth Annual Conference of the International Speech Communication Association 2015.","DOI":"10.21437\/Interspeech.2015-354"},{"key":"e_1_3_1_14_2","doi-asserted-by":"crossref","unstructured":"S.\u00a0Wang T.\u00a0Tuor T.\u00a0Salonidis K.K.\u00a0Leung and K.\u00a0Chan When edge meets learning: Adaptive control for resource constrained distributed machine learning IEEE INFOCOM 2018.","DOI":"10.1109\/INFOCOM.2018.8486403"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2014.2353996"},{"key":"e_1_3_1_16_2","doi-asserted-by":"crossref","unstructured":"K.\u00a0Zhang M.\u00a0Hutter and H.\u00a0Jin A newlocal distance-based outlier detection approach for scattered real-world data in: PAKDD Springer 2009 pp.\u00a0813\u2013822.","DOI":"10.1007\/978-3-642-01307-2_84"},{"key":"e_1_3_1_17_2","unstructured":"L.\u00a0Zhu Z.\u00a0Liu and S.\u00a0Han Deep leakage from gradients in: Advances in Neural Information Processing Systems 2019 pp.\u00a014774\u201314784."}],"container-title":["Web Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/WEB-210475","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/WEB-210475","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/WEB-210475","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T05:27:28Z","timestamp":1777613248000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/WEB-210475"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12]]},"references-count":16,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["10.3233\/WEB-210475"],"URL":"https:\/\/doi.org\/10.3233\/web-210475","relation":{},"ISSN":["2405-6456","2405-6464"],"issn-type":[{"value":"2405-6456","type":"print"},{"value":"2405-6464","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12]]}}}