{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T15:44:37Z","timestamp":1781019877611,"version":"3.54.1"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"13","license":[{"start":{"date-parts":[[2025,9,22]],"date-time":"2025-09-22T00:00:00Z","timestamp":1758499200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,9,22]],"date-time":"2025-09-22T00:00:00Z","timestamp":1758499200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001778","name":"Deakin University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001778","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cluster Comput"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Wireless Sensor Networks (WSNs) play a pivotal role in modern applications, ranging from smart cities to environmental monitoring. However, their inherent vulnerability to security threats, such as denial-of-service attacks and unauthorized access, necessitates the implementation of robust intrusion detection systems (IDS). Traditional IDS frameworks are predominantly centralized, raising significant concerns about data privacy, leakage, and scalability. In this paper, we propose a novel federated learning-based Random Forest architecture (RF-FedAvg) for intrusion detection in WSNs that addresses these limitations. Our method enables decentralized model training across multiple clients without sharing raw data, thereby enhancing privacy and mitigating data leakage. Random Forest models are trained locally on each client and aggregated using a weighted FedAvg strategy at the central server. To reflect real-world deployment scenarios, we evaluate the model under varying client configurations (2 to 5 nodes) and incorporate data balancing techniques such as Random Under Sampling (RUS) and SMOTE to address class imbalance issues common in WSN datasets. Extensive experiments on WSN-DS and UNSW-NB15 datasets demonstrate that the RF-FedAvg model maintains high performance even with fewer clients and imbalanced data. The highest accuracy achieved was 99.67% on WSN-DS with SMOTE and 98.45% on UNSW-NB15, showcasing strong robustness and scalability. These results confirm the effectiveness of our federated learning-based IDS in providing a scalable, privacy-preserving, and reliable solution for intrusion detection in resource-constrained WSNs environments.<\/jats:p>","DOI":"10.1007\/s10586-025-05591-8","type":"journal-article","created":{"date-parts":[[2025,9,22]],"date-time":"2025-09-22T12:05:47Z","timestamp":1758542747000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["RF-FedAvg: Federated learning-based random forest model for intrusion detection in wireless sensor networks"],"prefix":"10.1007","volume":"28","author":[{"given":"Ansam","family":"Khraisat","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md. Alamin","family":"Talukder","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md. Ashraf","family":"Uddin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ammar","family":"Alazab","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,9,22]]},"reference":[{"issue":"1","key":"5591_CR1","doi-asserted-by":"publisher","first-page":"5242517","DOI":"10.1155\/js\/5242517","volume":"2025","author":"KS Adu-Manu","year":"2025","unstructured":"Adu-Manu, K.S., Amoako, E., Engmann, F.: Advancements in machine learning-enhanced green wireless sensor networks: A comprehensive survey on energy efficiency, network performance, and future directions. J. Sens. 2025(1), 5242517 (2025)","journal-title":"J. Sens."},{"key":"5591_CR2","doi-asserted-by":"publisher","first-page":"1336088","DOI":"10.3389\/fenvs.2024.1336088","volume":"12","author":"SM Popescu","year":"2024","unstructured":"Popescu, S.M., Mansoor, S., Wani, O.A., Kumar, S.S., Sharma, V., Sharma, A., Arya, V.M., Kirkham, M., Hou, D., Bolan, N., et al.: Artificial intelligence and iot driven technologies for environmental pollution monitoring and management. Front. Environ. Sci. 12, 1336088 (2024)","journal-title":"Front. Environ. Sci."},{"key":"5591_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.adhoc.2024.103590","volume":"163","author":"N Xie","year":"2024","unstructured":"Xie, N., Zhang, C., Yuan, Q., Kong, J., Di, X.: Iov-bcfl: An intrusion detection method for iov based on blockchain and federated learning. Ad Hoc Networks 163, 103590 (2024)","journal-title":"Ad Hoc Networks"},{"key":"5591_CR4","doi-asserted-by":"crossref","unstructured":"Ahmed, A., Oluomachi, E., Abdullah, A., Tochukwu, N.: Enhancing data privacy in wireless sensor networks: Investigating techniques and protocols to protect privacy of data transmitted over wireless sensor networks in critical applications of healthcare and national security. arXiv preprint arXiv:2404.11388 (2024)","DOI":"10.5121\/ijnsa.2024.16204"},{"issue":"4","key":"5591_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3706583","volume":"57","author":"A Oztoprak","year":"2024","unstructured":"Oztoprak, A., Hassanpour, R., Ozkan, A., Oztoprak, K.: Security challenges, mitigation strategies, and future trends in wireless sensor networks: A review. ACM Comput. Surv. 57(4), 1\u201329 (2024)","journal-title":"ACM Comput. Surv."},{"issue":"4","key":"5591_CR6","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1007\/s42979-024-02725-4","volume":"5","author":"S Darla","year":"2024","unstructured":"Darla, S., Naveena, C.: Improved adaptive spiral seagull optimizer for intrusion detection and mitigation in wireless sensor network. SN Comput. Sci. 5(4), 394 (2024)","journal-title":"SN Comput. Sci."},{"key":"5591_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.adhoc.2024.103737","volume":"169","author":"W Aljabri","year":"2025","unstructured":"Aljabri, W., Hamid, M.A., Mosli, R.: Enhancing real-time intrusion detection system for in-vehicle networks by employing novel feature engineering techniques and lightweight modeling. Ad Hoc Networks 169, 103737 (2025)","journal-title":"Ad Hoc Networks"},{"key":"5591_CR8","doi-asserted-by":"publisher","first-page":"108006","DOI":"10.1016\/j.comcom.2024.108006","volume":"229","author":"MA Uddin","year":"2025","unstructured":"Uddin, M.A., Aryal, S., Bouadjenek, M.R., Al-Hawawreh, M., Talukder, M.A.: A dual-tier adaptive one-class classification ids for emerging cyberthreats. Comput. Commun. 229, 108006 (2025)","journal-title":"Comput. Commun."},{"issue":"2","key":"5591_CR9","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1007\/s11276-023-03516-0","volume":"30","author":"A A Bhutta","year":"2024","unstructured":"Bhutta, A. A., Nisa, M. u, Mian, A. N.: Lightweight real-time wifi-based intrusion detection system using lightgbm. Wirel. Netw. 30(2), 749\u2013761 (2024)","journal-title":"Wirel. Netw."},{"issue":"1","key":"5591_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-025-03498-3","volume":"15","author":"VK Pandey","year":"2025","unstructured":"Pandey, V.K., Prakash, S., Gupta, T.K., Sinha, P., Yang, T., Rathore, R.S., Wang, L., Tahir, S., Bakhsh, S.T.: Enhancing intrusion detection in wireless sensor networks using a tabu search based optimized random forest. Sci. Rep. 15(1), 1\u201321 (2025)","journal-title":"Sci. Rep."},{"key":"5591_CR11","doi-asserted-by":"crossref","unstructured":"Yang, K., Wang, J., Zhao, G., Wang, X., Cong, W., Yuan, M., Luo, J., Dong, X., Wang, J., Tao, J.: Nids-cnnrf integrating cnn and random forest for efficient network intrusion detection model. Internet of Things, 101607 (2025)","DOI":"10.1016\/j.iot.2025.101607"},{"issue":"1","key":"5591_CR12","doi-asserted-by":"publisher","first-page":"4617","DOI":"10.1038\/s41598-025-87028-1","volume":"15","author":"MA Talukder","year":"2025","unstructured":"Talukder, M.A., Khalid, M., Sultana, N.: A hybrid machine learning model for intrusion detection in wireless sensor networks leveraging data balancing and dimensionality reduction. Sci. Rep. 15(1), 4617 (2025)","journal-title":"Sci. Rep."},{"issue":"1","key":"5591_CR13","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1002\/spy2.499","volume":"8","author":"N Ahmed","year":"2025","unstructured":"Ahmed, N., Ngadi, M.A., Rathore, M.S., Mahmood, A.: Pcm-rf a hybrid feature selection mechanism for intrusion detection system in iot. Secur. Privacy 8(1), 499 (2025)","journal-title":"Secur. Privacy"},{"issue":"1","key":"5591_CR14","doi-asserted-by":"publisher","first-page":"19610","DOI":"10.48084\/etasr.9464","volume":"15","author":"SW Nourildean","year":"2025","unstructured":"Nourildean, S.W., Mefteh, W., Frihida, A.M.: Dtxg-rf-based intrusion detection system for artificial iot cyber attacks. Eng. Technol. Appl. Sci. Res. 15(1), 19610\u201319614 (2025)","journal-title":"Eng. Technol. Appl. Sci. Res."},{"issue":"1","key":"5591_CR15","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1007\/s10922-023-09794-5","volume":"32","author":"ARA Moundounga","year":"2024","unstructured":"Moundounga, A.R.A., Satori, H.: Stochastic machine learning based attacks detection system in wireless sensor networks. J. Netw. Syst. Manag. 32(1), 17 (2024)","journal-title":"J. Netw. Syst. Manag."},{"key":"5591_CR16","doi-asserted-by":"publisher","first-page":"131749","DOI":"10.1109\/ACCESS.2023.3335124","volume":"11","author":"FF Alruwaili","year":"2023","unstructured":"Alruwaili, F.F., Asiri, M.M., Alrayes, F.S., Aljameel, S.S., Salama, A.S., Hilal, A.M.: Red kite optimization algorithm with average ensemble model for intrusion detection for secure iot. IEEE Access 11, 131749\u2013131758 (2023)","journal-title":"IEEE Access"},{"key":"5591_CR17","unstructured":"Meng, D., Dai, H., Sun, Q., Xu, Y., Shi, T.: Novel wireless sensor network intrusion detection method based on lightgbm model. IAENG  Int. J. Appl. Math 52(4), 955\u2013961 (2022)"},{"issue":"8938","key":"5591_CR18","first-page":"8938","volume":"2252","author":"P Chandre","year":"2022","unstructured":"Chandre, P., Mahalle, P., Shinde, G.: Intrusion prevention system using convolutional neural network for wireless sensor network. Int J Artif Intell ISSN 2252(8938), 8938 (2022)","journal-title":"Int J Artif Intell ISSN"},{"issue":"4","key":"5591_CR19","first-page":"281","volume":"12","author":"NM Alruhaily","year":"2021","unstructured":"Alruhaily, N.M., Ibrahim, D.M.: A multi-layer machine learning-based intrusion detection system for wireless sensor networks. Int. J. Adv. Comput. Sci. Appl. 12(4), 281\u2013288 (2021)","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"5591_CR20","doi-asserted-by":"crossref","unstructured":"Ifzarne, S., Tabbaa, H., Hafidi, I., Lamghari, N.: Anomaly detection using machine learning techniques in wireless sensor networks. In: Journal of Physics: Conference Series, vol. 1743, p. 012021 (2021). IOP Publishing","DOI":"10.1088\/1742-6596\/1743\/1\/012021"},{"issue":"03","key":"5591_CR21","doi-asserted-by":"publisher","first-page":"2050018","DOI":"10.1142\/S1469026820500182","volume":"19","author":"N Singh","year":"2020","unstructured":"Singh, N., Virmani, D., Gao, X.-Z.: A fuzzy logic-based method to avert intrusions in wireless sensor networks using wsn-ds dataset. Int. J. Comput. Intell. Appl. 19(03), 2050018 (2020)","journal-title":"Int. J. Comput. Intell. Appl."},{"key":"5591_CR22","doi-asserted-by":"crossref","unstructured":"Saleh, H.M., Marouane, H., Fakhfakh, A.: Stochastic gradient descent intrusions detection for wireless sensor network attack detection system using machine learning. IEEE Access 22, 3825\u20133836 (2024)","DOI":"10.1109\/ACCESS.2023.3349248"},{"key":"5591_CR23","doi-asserted-by":"crossref","unstructured":"Rana, A., Prajapat, S., Kumar, P., Kumar, K.: Performance evaluation of machine learning models for intrusion detection in wireless sensor networks: A case study using the wsn ds dataset. In: International Conference on MAchine inTelligence for Research & Innovations, pp. 173\u2013180 (2023). Springer","DOI":"10.1007\/978-981-99-8129-8_15"},{"key":"5591_CR24","first-page":"100684","volume":"22","author":"SE Quincozes","year":"2023","unstructured":"Quincozes, S.E., Kazienko, J.F., Quincozes, V.E.: An extended evaluation on machine learning techniques for denial-of-service detection in wireless sensor networks. IoT 22, 100684 (2023)","journal-title":"IoT"},{"key":"5591_CR25","doi-asserted-by":"crossref","unstructured":"Elsadig, M.A.: Detection of denial-of-service attack in wireless sensor networks: A lightweight machine learning approach. IEEE Access 11, 83537\u201383552 (2023)","DOI":"10.1109\/ACCESS.2023.3303113"},{"key":"5591_CR26","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1016\/j.ins.2019.10.069","volume":"513","author":"MM Hassan","year":"2020","unstructured":"Hassan, M.M., Gumaei, A., Alsanad, A., Alrubaian, M., Fortino, G.: A hybrid deep learning model for efficient intrusion detection in big data environment. Inf. Sci. 513, 386\u2013396 (2020)","journal-title":"Inf. Sci."},{"key":"5591_CR27","doi-asserted-by":"publisher","first-page":"107315","DOI":"10.1016\/j.comnet.2020.107315","volume":"177","author":"H Zhang","year":"2020","unstructured":"Zhang, H., Huang, L., Wu, C.Q., Li, Z.: An effective convolutional neural network based on smote and gaussian mixture model for intrusion detection in imbalanced dataset. Comput. Netw. 177, 107315 (2020)","journal-title":"Comput. Netw."},{"key":"5591_CR28","doi-asserted-by":"publisher","first-page":"1561","DOI":"10.1016\/j.procs.2020.03.367","volume":"167","author":"S Choudhary","year":"2020","unstructured":"Choudhary, S., Kesswani, N.: Analysis of kdd-cup\u201999, nsl-kdd and unsw-nb15 datasets using deep learning in iot. Procedia Computer Science 167, 1561\u20131573 (2020)","journal-title":"Procedia Computer Science"},{"issue":"1","key":"5591_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-020-00379-6","volume":"7","author":"SM Kasongo","year":"2020","unstructured":"Kasongo, S.M., Sun, Y.: Performance analysis of intrusion detection systems using a feature selection method on the unsw-nb15 dataset. Journal of Big Data 7(1), 1\u201320 (2020)","journal-title":"Journal of Big Data"},{"issue":"1","key":"5591_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13638-021-01893-8","volume":"2021","author":"M Ahmad","year":"2021","unstructured":"Ahmad, M., Riaz, Q., Zeeshan, M., Tahir, H., Haider, S.A., Khan, M.S.: Intrusion detection in internet of things using supervised machine learning based on application and transport layer features using unsw-nb15 data-set. EURASIP Journal on Wireless Communications and Networking 2021(1), 1\u201323 (2021)","journal-title":"EURASIP Journal on Wireless Communications and Networking"},{"key":"5591_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2020.101752","volume":"92","author":"SM Kasongo","year":"2020","unstructured":"Kasongo, S.M., Sun, Y.: A deep learning method with wrapper based feature extraction for wireless intrusion detection system. Computers & Security 92, 101752 (2020)","journal-title":"Computers & Security"},{"key":"5591_CR32","doi-asserted-by":"crossref","unstructured":"Hassouneh, N., Al-sharaeh, S.: Intrusion detection in iot networks using lstm deep learning models with the unsw-nb15 dataset. In: 2025 International Conference on New Trends in Computing Sciences (ICTCS), pp. 263\u2013269 (2025). IEEE","DOI":"10.1109\/ICTCS65341.2025.10989358"},{"issue":"3","key":"5591_CR33","doi-asserted-by":"publisher","first-page":"70033","DOI":"10.1002\/spy2.70033","volume":"8","author":"L Jiang","year":"2025","unstructured":"Jiang, L.: A network anomaly traffic detection method based on cnn-lstm. Secur. Privacy 8(3), 70033 (2025)","journal-title":"Secur. Privacy"},{"issue":"2","key":"5591_CR34","doi-asserted-by":"publisher","first-page":"0316253","DOI":"10.1371\/journal.pone.0316253","volume":"20","author":"AA Y\u0131lmaz","year":"2025","unstructured":"Y\u0131lmaz, A.A.: A novel deep learning-based framework with particle swarm optimisation for intrusion detection in computer networks. PloS one 20(2), 0316253 (2025)","journal-title":"PloS one"},{"key":"5591_CR35","doi-asserted-by":"crossref","unstructured":"Jouhari, M., Benaddi, H., Ibrahimi, K.: Efficient intrusion detection: Combining x 2 feature selection with cnn-bilstm on the unsw-nb15 dataset. In: 2024 11th International Conference on Wireless Networks and Mobile Communications (WINCOM), pp. 1\u20136 (2024). IEEE","DOI":"10.1109\/WINCOM62286.2024.10658099"},{"issue":"1","key":"5591_CR36","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s10922-022-09691-3","volume":"31","author":"M Sarhan","year":"2023","unstructured":"Sarhan, M., Layeghy, S., Moustafa, N., Portmann, M.: Cyber threat intelligence sharing scheme based on federated learning for network intrusion detection. J. Netw. Syst. Manag. 31(1), 3 (2023)","journal-title":"J. Netw. Syst. Manag."},{"key":"5591_CR37","doi-asserted-by":"publisher","first-page":"2751","DOI":"10.7717\/peerj-cs.2751","volume":"11","author":"RW Anwar","year":"2025","unstructured":"Anwar, R.W., Abrar, M., Salam, A., Ullah, F.: Federated learning with lstm for intrusion detection in iot-based wireless sensor networks: a multi-dataset analysis. PeerJ Comput. Sci. 11, 2751 (2025)","journal-title":"PeerJ Comput. Sci."}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-025-05591-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-025-05591-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-025-05591-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T17:56:43Z","timestamp":1762192603000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-025-05591-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,22]]},"references-count":37,"journal-issue":{"issue":"13","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["5591"],"URL":"https:\/\/doi.org\/10.1007\/s10586-025-05591-8","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"value":"1386-7857","type":"print"},{"value":"1573-7543","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,22]]},"assertion":[{"value":"30 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 June 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 June 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 September 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflicts of interest to declare that they are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"873"}}