{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T07:19:34Z","timestamp":1768547974181,"version":"3.49.0"},"reference-count":39,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T00:00:00Z","timestamp":1639699200000},"content-version":"vor","delay-in-days":350,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>Federated learning (FL) is an emerging subdomain of machine learning (ML) in a distributed and heterogeneous setup. It provides efficient training architecture, sufficient data, and privacy\u2010preserving communication for boosting the performance and feasibility of ML algorithms. In this environment, the resultant global model produced by averaging various trained client models is vital. During each round of FL, model parameters are transferred from each client device to the server while the server waits for all models before it can average them. In a realistic scenario, waiting for all clients to communicate their model parameters, where client models are trained on low\u2010power Internet of Things (IoT) devices, can result in a deadlock. In this paper, a novel temporal model averaging algorithm is proposed for asynchronous federated learning (AFL). Our approach uses a dynamic expectation function that computes the number of client models expected in each round and a weighted averaging algorithm for continuous modification of the global model. This ensures that the federated architecture is not stuck in a deadlock all the while increasing the throughput of the server and clients. To implicate the importance of asynchronicity in cybersecurity, the proposed algorithm is tested using NSL\u2010KDD intrusion detection system datasets. The performance accuracy of the global model is about 99.5% on the dataset, outperforming traditional FL models in anomaly detection. In terms of asynchronicity, we get an increased throughput of almost 10.17% for every 30 timesteps.<\/jats:p>","DOI":"10.1155\/2021\/5844728","type":"journal-article","created":{"date-parts":[[2021,12,18]],"date-time":"2021-12-18T02:50:13Z","timestamp":1639795813000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Temporal Weighted Averaging for Asynchronous Federated Intrusion Detection Systems"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0006-267X","authenticated-orcid":false,"given":"Shaashwat","family":"Agrawal","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0734-3535","authenticated-orcid":false,"given":"Aditi","family":"Chowdhuri","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2030-9438","authenticated-orcid":false,"given":"Sagnik","family":"Sarkar","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6822-499X","authenticated-orcid":false,"given":"Ramani","family":"Selvanambi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0097-801X","authenticated-orcid":false,"given":"Thippa Reddy","family":"Gadekallu","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,12,17]]},"reference":[{"key":"e_1_2_8_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/tvt.2020.3027568"},{"key":"e_1_2_8_2_2","doi-asserted-by":"crossref","unstructured":"KumariU.andSoniU. A review of intrusion detection using anomaly based detection Proceedings of the 2017 2nd International Conference on Communication and Electronics Systems (ICCES) October 2017 Coimbatore India 824\u2013826 https:\/\/doi.org\/10.1109\/cesys.2017.8321199 2-s2.0-85047117391.","DOI":"10.1109\/CESYS.2017.8321199"},{"key":"e_1_2_8_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.105124"},{"key":"e_1_2_8_4_2","doi-asserted-by":"crossref","unstructured":"KaratasG. DemirO. andSahingozO. K. Deep learning in intrusion detection systems Proceedings of the 2018 International Congress on Big Data Deep Learning and Fighting Cyber Terrorism (IBIGDELFT) December 2018 Ankara Turkey 113\u2013116 https:\/\/doi.org\/10.1109\/ibigdelft.2018.8625278 2-s2.0-85062689458.","DOI":"10.1109\/IBIGDELFT.2018.8625278"},{"key":"e_1_2_8_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2018.2869577"},{"key":"e_1_2_8_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2020.2972627"},{"key":"e_1_2_8_7_2","article-title":"Intrusion Detection for Cyber-Physical Systems Using Generative Adversarial Networks in Fog Environment","volume":"8","author":"Freitas de Araujo-Filho P.","year":"2020","journal-title":"IEEE Internet of Things Journal"},{"key":"e_1_2_8_8_2","doi-asserted-by":"crossref","unstructured":"TavallaeeM. BagheriE. LuW. andGhorbaniA. A. A detailed analysis of the kdd cup 99 data set Proceedings of the 2009 IEEE symposium on computational intelligence for security and defense applications July 2009 Ottawa ON Canada 1\u20136 https:\/\/doi.org\/10.1109\/cisda.2009.5356528 2-s2.0-77950575061.","DOI":"10.1109\/CISDA.2009.5356528"},{"key":"e_1_2_8_9_2","doi-asserted-by":"crossref","unstructured":"SharafaldinI. Habibi LashkariA. andGhorbaniA. A. Toward generating a new intrusion detection dataset and intrusion traffic characterization Proceedings of the 4th International Conference on Information Systems Security and Privacy January 2018 Funchal Madeira Portugal 108\u2013116 https:\/\/doi.org\/10.5220\/0006639801080116.","DOI":"10.5220\/0006639801080116"},{"key":"e_1_2_8_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2019.05.041"},{"key":"e_1_2_8_11_2","doi-asserted-by":"crossref","unstructured":"CetinB. LazarA. KimJ. SimA. andWuK. Federated wireless network intrusion detection Proceedings of the 2019 IEEE International Conference on Big Data (Big Data) December 2019 Los Angeles CA USA 6004\u20136006 https:\/\/doi.org\/10.1109\/bigdata47090.2019.9005507.","DOI":"10.1109\/BigData47090.2019.9005507"},{"key":"e_1_2_8_12_2","unstructured":"PhamQ. V. DevK. MaddikuntaP. K. R. GadekalluT. R. andHuynh-TheT. Fusion of federated learning and industrial internet of things: a survey 2021 https:\/\/arxiv.org\/abs\/2101.00798."},{"key":"e_1_2_8_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3077803"},{"key":"e_1_2_8_14_2","unstructured":"SarkarS. AouediO. YenduriG. PiamratK. BhattacharyaS. andGadekalluT. R. Federated learning for intrusion detection system: concepts challenges and future directions 2021 https:\/\/arxiv.org\/abs\/2106.09527."},{"key":"e_1_2_8_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/tii.2021.3119038"},{"key":"e_1_2_8_16_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-13057-2"},{"key":"e_1_2_8_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2020.3041793"},{"key":"e_1_2_8_18_2","doi-asserted-by":"crossref","unstructured":"ChenY. NingY. MartinS. andRangwalaH. Asynchronous online federated learning for edge devices with non-iid data Proceedings of the 2020 IEEE International Conference on Big Data (Big Data) December 2020 Atlanta GA USA 15\u201324 https:\/\/doi.org\/10.1109\/bigdata50022.2020.9378161.","DOI":"10.1109\/BigData50022.2020.9378161"},{"key":"e_1_2_8_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2942179"},{"key":"e_1_2_8_20_2","doi-asserted-by":"publisher","DOI":"10.1002\/ett.4121"},{"key":"e_1_2_8_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2020.2995133"},{"key":"e_1_2_8_22_2","unstructured":"AlkasassbehM.andAlmseidinM. Machine Learning Methods for Network Intrusion Detection Proceedings of the 20th International Conference on Computing Communication August 2018 Beijing China."},{"key":"e_1_2_8_23_2","doi-asserted-by":"crossref","unstructured":"BhavaniT. T. RaoM. K. andReddyA. M. Network intrusion detection system using random forest and decision tree machine learning techniques Proceedings of the first International Conference on Sustainable Technologies for Computational Intelligence March 2020 Jaipur Rajasthan India 637\u2013643 https:\/\/doi.org\/10.1007\/978-981-15-0029-9_50.","DOI":"10.1007\/978-981-15-0029-9_50"},{"key":"e_1_2_8_24_2","doi-asserted-by":"crossref","unstructured":"Gautam SrivastavaN. D. PrabadeviB. andPraveen Kumar ReddyM. An ensemble model for intrusion detection in the internet of softwarized things Proceedings of the 2021 International Conference on Distributed Computing and Networking January 2021 Nara Japan 25\u201330 https:\/\/doi.org\/10.1145\/3427477.3429987.","DOI":"10.1145\/3427477.3429987"},{"key":"e_1_2_8_25_2","doi-asserted-by":"crossref","unstructured":"ZamanM.andLungC.-H. Evaluation of machine learning techniques for network intrusion detection Proceedings of the NOMS 2018-2018 IEEE\/IFIP Network Operations and Management Symposium April 2018 Taipei Taiwan 1\u20135 https:\/\/doi.org\/10.1109\/noms.2018.8406212 2-s2.0-85050700411.","DOI":"10.1109\/NOMS.2018.8406212"},{"key":"e_1_2_8_26_2","doi-asserted-by":"publisher","DOI":"10.3390\/electronics9020219"},{"key":"e_1_2_8_27_2","doi-asserted-by":"crossref","unstructured":"VermaP. AnwarS. KhanS. andManeS. B. Network intrusion detection using clustering and gradient boosting Proceedings of the 2018 9th International Conference on Computing Communication and Networking Technologies (ICCCNT) July 2018 Bengaluru India 1\u20137 https:\/\/doi.org\/10.1109\/icccnt.2018.8494186 2-s2.0-85056842065.","DOI":"10.1109\/ICCCNT.2018.8494186"},{"key":"e_1_2_8_28_2","doi-asserted-by":"crossref","unstructured":"Abu TaherK. Yasin JisanB. M. andMahbubur RahmanM. d. Network intrusion detection using supervised machine learning technique with feature selection Proceedings of the 2019 International Conference on Robotics Electrical and Signal Processing Techniques (ICREST) January 2019 Dhaka Bangladesh 643\u2013646 https:\/\/doi.org\/10.1109\/icrest.2019.8644161 2-s2.0-85063076698.","DOI":"10.1109\/ICREST.2019.8644161"},{"key":"e_1_2_8_29_2","article-title":"An intelligent crf based feature selection for effective intrusion detection","volume":"13","author":"Ganapathy S.","year":"2016","journal-title":"International Arab Journal of Information Technology (IAJIT)"},{"key":"e_1_2_8_30_2","doi-asserted-by":"publisher","DOI":"10.3390\/app8122663"},{"key":"e_1_2_8_31_2","doi-asserted-by":"crossref","unstructured":"Duc NguyenT. MarchalS. MiettinenM. FereidooniH. andAsokanN. DIOT: a federated self-learning anomaly detection system for iot Proceedings of the 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS) July 2019 Dallas Texas USA 756\u2013767.","DOI":"10.1109\/ICDCS.2019.00080"},{"key":"e_1_2_8_32_2","doi-asserted-by":"crossref","unstructured":"ChenY. ZhangJ. andKiat YeoC. Network anomaly detection using federated deep autoencoding Gaussian mixture model Proceedings of the International Conference on Machine Learning for Networking June 2019 Long Beach CA USA 1\u201314.","DOI":"10.1007\/978-3-030-45778-5_1"},{"key":"e_1_2_8_33_2","doi-asserted-by":"crossref","unstructured":"ZhaoY. ChenJ. WuD. TengJ. andYuS. Multi-task network anomaly detection using federated learning Proceedings of the Tenth International Symposium on Information and Communication Technology December 2019 Hanoi Ha Long Bay Vietnam 273\u2013279 https:\/\/doi.org\/10.1145\/3368926.3369705.","DOI":"10.1145\/3368926.3369705"},{"key":"e_1_2_8_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2020.2978082"},{"key":"e_1_2_8_35_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2953131"},{"key":"e_1_2_8_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/MCE.2020.3040541"},{"key":"e_1_2_8_37_2","unstructured":"Al-Marri NoorA. Al-A. Bekir CiftlerS. andAbdallahM. M. Federated mimic learning for privacy preserving intrusion detection Proceedings of the 2020 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom) May 2020 Odessa Ukraine 1\u20136."},{"key":"e_1_2_8_38_2","doi-asserted-by":"crossref","unstructured":"KimS. HeC. HuaC. GuP. andXuW. Collaborative anomaly detection for internet of things based on federated learning Proceedings of the 2020 IEEE\/CIC International Conference on Communications in China (ICCC) August 2020 Chongqing China 623\u2013628 https:\/\/doi.org\/10.1109\/iccc49849.2020.9238913.","DOI":"10.1109\/ICCC49849.2020.9238913"},{"key":"e_1_2_8_39_2","unstructured":"ChatterjeeS.andHanawalM. K. Federated learning for intrusion detection in IoT security: a hybrid ensemble approach 2021 https:\/\/arxiv.org\/abs\/2106.15349."}],"container-title":["Computational Intelligence and Neuroscience"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2021\/5844728.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2021\/5844728.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2021\/5844728","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T11:36:25Z","timestamp":1722944185000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2021\/5844728"}},"subtitle":[],"editor":[{"given":"Qiangqiang","family":"Yuan","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2021,1]]},"references-count":39,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["10.1155\/2021\/5844728"],"URL":"https:\/\/doi.org\/10.1155\/2021\/5844728","archive":["Portico"],"relation":{},"ISSN":["1687-5265","1687-5273"],"issn-type":[{"value":"1687-5265","type":"print"},{"value":"1687-5273","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1]]},"assertion":[{"value":"2021-08-19","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-11-24","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-12-17","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"5844728"}}