{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T16:48:40Z","timestamp":1776358120575,"version":"3.51.2"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2025,6,16]],"date-time":"2025-06-16T00:00:00Z","timestamp":1750032000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,6,16]],"date-time":"2025-06-16T00:00:00Z","timestamp":1750032000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cluster Comput"],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1007\/s10586-024-05091-1","type":"journal-article","created":{"date-parts":[[2025,6,16]],"date-time":"2025-06-16T15:02:31Z","timestamp":1750086151000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Sfedrl-ids: secure federated deep reinforcement learning-based intrusion detection system for agricultural internet of things"],"prefix":"10.1007","volume":"28","author":[{"given":"Rabaie","family":"Benameur","sequence":"first","affiliation":[]},{"given":"Amine","family":"Dahane","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,6,16]]},"reference":[{"key":"5091_CR1","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1016\/j.comcom.2023.06.020","volume":"208","author":"SH Mekala","year":"2023","unstructured":"Mekala, S.H., Baig, Z., Anwar, A., Zeadally, S.: Cybersecurity for industrial IoT (IIoT): threats, countermeasures, challenges and future directions. Comput. Commun. 208, 294\u2013320 (2023). https:\/\/doi.org\/10.1016\/j.comcom.2023.06.020","journal-title":"Comput. Commun."},{"key":"5091_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2020.102136","volume":"102","author":"K-KR Choo","year":"2021","unstructured":"Choo, K.-K.R., Gai, K., Chiaraviglio, L., Yang, Q.: A multidisciplinary approach to internet of things (IoT) cybersecurity and risk management. Comput. Secur. 102, 102136 (2021). https:\/\/doi.org\/10.1016\/j.cose.2020.102136","journal-title":"Comput. Secur."},{"key":"5091_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2020.107688","volume":"184","author":"A Yazdinejadna","year":"2021","unstructured":"Yazdinejadna, A., Parizi, R.M., Dehghantanha, A., Khan, M.S.: A kangaroo-based intrusion detection system on software-defined networks. Comput. Netw. 184, 107688 (2021)","journal-title":"Comput. Netw."},{"key":"5091_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2022\/9023719","volume":"2022","author":"S Tharewal","year":"2022","unstructured":"Tharewal, S., Ashfaque, M.W., Banu, S.S., Uma, P., Hassen, S.M., Shabaz, M.: Intrusion detection system for industrial internet of things based on deep reinforcement learning. Wirel. Commun. Mob. Comput. 2022, 1\u20138 (2022)","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"5091_CR5","doi-asserted-by":"crossref","unstructured":"Suwannalai, E., Polprasert, C.: Network intrusion detection systems using adversarial reinforcement learning with deep q-network. In: 2020 18th International Conference on ICT and Knowledge Engineering (ICT). IEEE, (2020)","DOI":"10.1109\/ICTKE50349.2020.9289884"},{"key":"5091_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.adhoc.2022.103080","volume":"141","author":"A Sadiki","year":"2023","unstructured":"Sadiki, A., Bentahar, J., Dssouli, R., En-Nouaary, A., Otrok, H.: Deep reinforcement learning for the computation offloading in MIMO-based edge computing. Ad Hoc Netw. 141, 103080 (2023)","journal-title":"Ad Hoc Netw."},{"key":"5091_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmcj.2023.101819","volume":"93","author":"NL Gim\u00e9nez","year":"2023","unstructured":"Gim\u00e9nez, N.L., Sol\u00e9, J.M., Freitag, F.: Embedded federated learning over a LoRa mesh network. Pervasive Mob. Comput. 93, 101819 (2023)","journal-title":"Pervasive Mob. Comput."},{"key":"5091_CR8","doi-asserted-by":"crossref","unstructured":"Ferrag, M.A., Friha, O., Hamouda, D., Maglaras, L., Janicke, H.: Edge-IIoTset: a new comprehensive realistic cyber security dataset of IoT and IIoT applications for centralized and federated learning. (2022)","DOI":"10.36227\/techrxiv.18857336.v1"},{"issue":"1","key":"5091_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13677-018-0123-6","volume":"7","author":"MF Elrawy","year":"2018","unstructured":"Elrawy, M.F., Awad, A.I., Hamed, H.F.A.: Intrusion detection systems for IoT-based smart environments: a survey. J. Cloud Comput. 7(1), 1\u201320 (2018)","journal-title":"J. Cloud Comput."},{"key":"5091_CR10","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.jnca.2017.02.009","volume":"84","author":"BB Zarpel\u00e3o","year":"2017","unstructured":"Zarpel\u00e3o, B.B., Miani, R.S., Kawakani, C.T., de Alvarenga, S.C.: A survey of intrusion detection in internet of things. J. Netw. Comput. Appl. 84, 25\u201337 (2017)","journal-title":"J. Netw. Comput. Appl."},{"key":"5091_CR11","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1016\/j.future.2022.03.007","volume":"133","author":"Y Zhang","year":"2022","unstructured":"Zhang, Y., Liu, Q.: On IoT intrusion detection based on data augmentation for enhancing learning on unbalanced samples. Futur. Gener. Comput. Syst. 133, 213\u2013227 (2022)","journal-title":"Futur. Gener. Comput. Syst."},{"key":"5091_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2022.102684","volume":"117","author":"A Chohra","year":"2022","unstructured":"Chohra, A., Shirani, P., Karbab, E.B., Debbabi, M.: Chameleon: optimized feature selection using particle swarm optimization and ensemble methods for network anomaly detection. Comput. Secur. 117, 102684 (2022)","journal-title":"Comput. Secur."},{"key":"5091_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2022.102908","volume":"122","author":"J-S Lee","year":"2022","unstructured":"Lee, J.-S., Chen, Y.-C., Chew, C.-J., Chen, C.-L., Huynh, T.-N., Kuo, C.-W.: CoNN-IDS: Intrusion detection system based on collaborative neural networks and agile training. Comput. Secur. 122, 102908 (2022)","journal-title":"Comput. Secur."},{"issue":"16","key":"5091_CR14","first-page":"26866","volume":"11","author":"D Javeed","year":"2024","unstructured":"Javeed, D., Gao, T., Saeed, M.S., Kumar, P.: An intrusion detection system for edge-envisioned smart agriculture in extreme environment. IEEE Int. Things J. 11(16), 26866\u201326876 (2024)","journal-title":"IEEE Int. Things J."},{"key":"5091_CR15","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.jpdc.2022.03.003","volume":"165","author":"O Friha","year":"2022","unstructured":"Friha, O., Ferrag, M.A., Shu, L., Maglaras, L., Choo, K.-K.R., Nafaa, M.: FELIDS: Federated learning-based intrusion detection system for agricultural internet of things. J. Parallel Distrib. Comput. 165, 17\u201331 (2022)","journal-title":"J. Parallel Distrib. Comput."},{"key":"5091_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.adhoc.2024.103540","volume":"162","author":"D Javeed","year":"2024","unstructured":"Javeed, D., Saeed, M.S., Adil, M., Kumar, P., Jolfaei, A.: A federated learning-based zero trust intrusion detection system for internet of things. Ad Hoc Netw. 162, 103540 (2024)","journal-title":"Ad Hoc Netw."},{"key":"5091_CR17","doi-asserted-by":"crossref","unstructured":"Benameur, R., Dahane, A., Souihi, S., Mellouk, A.: A novel federated learning based intrusion detection system for iot networks. In: ICC 2024 - IEEE International Conference on Communications. IEEE, pp. 2402\u20132407 (2024)","DOI":"10.1109\/ICC51166.2024.10622538"},{"issue":"11","key":"5091_CR18","doi-asserted-by":"publisher","first-page":"8356","DOI":"10.1109\/TII.2022.3168011","volume":"18","author":"A Yazdinejad","year":"2022","unstructured":"Yazdinejad, A., Dehghantanha, A., Parizi, R.M., Hammoudeh, M., Karimipour, H., Srivastava, G.: Block hunter: federated learning for cyber threat hunting in blockchain-based iiot networks. IEEE Trans. Industr. Inf. 18(11), 8356\u20138366 (2022)","journal-title":"IEEE Trans. Industr. Inf."},{"key":"5091_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.112963","volume":"141","author":"M Lopez-Martin","year":"2020","unstructured":"Lopez-Martin, M., Carro, B., Sanchez-Esguevillas, A.: Application of deep reinforcement learning to intrusion detection for supervised problems. Expert Syst. Appl. 141, 112963 (2020)","journal-title":"Expert Syst. Appl."},{"issue":"4","key":"5091_CR20","doi-asserted-by":"publisher","first-page":"1162","DOI":"10.3390\/s24041162","volume":"24","author":"R Benameur","year":"2024","unstructured":"Benameur, R., Dahane, A., Kechar, B., Benyamina, A.E.H.: An innovative smart and sustainable low-cost irrigation system for anomaly detection using deep learning. Sensors 24(4), 1162 (2024)","journal-title":"Sensors"},{"key":"5091_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2023.109696","volume":"226","author":"Z Zeng","year":"2023","unstructured":"Zeng, Z., Cui, L., Qian, M., Zhang, Z., Wei, K.: A survey on sliding window sketch for network measurement. Comput. Netw. 226, 109696 (2023)","journal-title":"Comput. Netw."},{"issue":"7","key":"5091_CR22","doi-asserted-by":"publisher","first-page":"325","DOI":"10.3390\/a16070325","volume":"16","author":"T Th\u00e9ate","year":"2023","unstructured":"Th\u00e9ate, T., Ernst, D.: Risk-sensitive policy with distributional reinforcement learning. Algorithms 16(7), 325 (2023)","journal-title":"Algorithms"},{"issue":"6","key":"5091_CR23","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1109\/MSP.2017.2743240","volume":"34","author":"K Arulkumaran","year":"2017","unstructured":"Arulkumaran, K., Deisenroth, M.P., Brundage, M., Bharath, A.A.: Deep reinforcement learning: a brief survey. IEEE Signal Process. Mag. 34(6), 26\u201338 (2017)","journal-title":"IEEE Signal Process. Mag."},{"key":"5091_CR24","doi-asserted-by":"crossref","unstructured":"Cai, Q., Cui, C., Xiong, Y., Wang, W., Xie, Z., Zhang, M.: A survey on deep reinforcement learning for data processing and analytics. IEEE Transactions on Knowledge and Data Engineering, 1\u20131 (2022)","DOI":"10.1109\/TKDE.2022.3155196"},{"issue":"14","key":"5091_CR25","doi-asserted-by":"publisher","first-page":"2739","DOI":"10.3390\/electronics13142739","volume":"13","author":"J Zhang","year":"2024","unstructured":"Zhang, J., Liu, Z.: Perfreezeclip: personalized federated learning based on adaptive clipping. Electronics 13(14), 2739 (2024)","journal-title":"Electronics"},{"key":"5091_CR26","doi-asserted-by":"crossref","unstructured":"Natarajan, D., Dai, W.: Seal-embedded: a homomorphic encryption library for the internet of things. IACR Transactions on Cryptographic Hardware and Embedded Systems, 756\u2013779 (2021)","DOI":"10.46586\/tches.v2021.i3.756-779"},{"issue":"1","key":"5091_CR27","doi-asserted-by":"publisher","first-page":"8","DOI":"10.3390\/cryptography8010008","volume":"8","author":"S Behera","year":"2024","unstructured":"Behera, S., Prathuri, J.R.: Fpga-based acceleration of k-nearest neighbor algorithm on fully homomorphic encrypted data. Cryptography 8(1), 8 (2024)","journal-title":"Cryptography"},{"key":"5091_CR28","doi-asserted-by":"crossref","unstructured":"Dahane, A., Benameur, R., Kechar, B., Benyamina, A.: An iot based smart farming system using machine learning. In: 2020 International Symposium on Networks, Computers and Communications (ISNCC). Montreal, QC, Canada pp. 1\u20136 (2020)","DOI":"10.1109\/ISNCC49221.2020.9297341"},{"issue":"12","key":"5091_CR29","doi-asserted-by":"publisher","first-page":"3173","DOI":"10.1007\/s11277-022-09915-4","volume":"127","author":"A Dahane","year":"2022","unstructured":"Dahane, A., Benameur, R., Kechar, B.: An iot low-cost smart farming for enhancing irrigation efficiency of smallholder farmers. Wireless Pers. Commun. 127(12), 3173\u20133210 (2022)","journal-title":"Wireless Pers. Commun."},{"key":"5091_CR30","doi-asserted-by":"crossref","unstructured":"Pham, C., Rahim, A., Hartmann, C., Dupont, C., Forster, J., Markwordt, F., Printanier, J.-F., Kechar, B., Benkhelifa, M., Baraka, K., Benabdelouahab, T., Bartzanas, T., Fountas, S.: Deploying low-cost and full edge-IoT\/AI system for optimizing irrigation in smallholder farmers communities. In: Ambient Intelligence and Smart Environments. IOS Press, (2022)","DOI":"10.3233\/AISE220030"},{"issue":"19","key":"5091_CR31","doi-asserted-by":"publisher","first-page":"9572","DOI":"10.3390\/app12199572","volume":"12","author":"I Tareq","year":"2022","unstructured":"Tareq, I., Elbagoury, B.M., El-Regaily, S., El-Horbaty, E.-S.M.: Analysis of ton-iot, unw-nb15, and edge-iiot datasets using dl in cybersecurity for iot. Appl. Sci. 12(19), 9572 (2022)","journal-title":"Appl. Sci."},{"key":"5091_CR32","doi-asserted-by":"crossref","unstructured":"Elias, E.M.d., Carriel, V.S., De\u00a0Oliveira, G.W., Dos\u00a0Santos, A.L., Nogueira, M., Junior, R.H., Batista, D.M.: A hybrid cnn-lstm model for iiot edge privacy-aware intrusion detection. In: 2022 IEEE Latin-American Conference on Communications (LATINCOM). IEEE (Nov. 2022)","DOI":"10.1109\/LATINCOM56090.2022.10000468"},{"issue":"1","key":"5091_CR33","doi-asserted-by":"publisher","first-page":"1342","DOI":"10.1109\/TCE.2023.3283704","volume":"70","author":"D Javeed","year":"2024","unstructured":"Javeed, D., Gao, T., Kumar, P., Jolfaei, A.: An explainable and resilient intrusion detection system for industry 5.0. IEEE Trans. Consum. Electron. 70(1), 1342\u20131350 (2024)","journal-title":"IEEE Trans. Consum. Electron."},{"key":"5091_CR34","doi-asserted-by":"crossref","unstructured":"Attique, D., Hao, W., Ping, W., Javeed, D., Kumar, P.: Explainable and data-efficient deep learning for enhanced attack detection in iiot ecosystem. IEEE Int Things J 1\u20131 (2024)","DOI":"10.1109\/JIOT.2024.3384374"},{"key":"5091_CR35","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/j.future.2024.02.015","volume":"156","author":"R Kumar","year":"2024","unstructured":"Kumar, R., Aljuhani, A., Javeed, D., Kumar, P., Islam, S., Islam, A.N.: Digital twins-enabled zero touch network: a smart contract and explainable ai integrated cybersecurity framework. Futur. Gener. Comput. Syst. 156, 191\u2013205 (2024)","journal-title":"Futur. Gener. Comput. Syst."},{"key":"5091_CR36","doi-asserted-by":"publisher","first-page":"6693","DOI":"10.1109\/TIFS.2024.3420126","volume":"19","author":"A Yazdinejad","year":"2024","unstructured":"Yazdinejad, A., Dehghantanha, A., Karimipour, H., Srivastava, G., Parizi, R.M.: A robust privacy-preserving federated learning model against model poisoning attacks. IEEE Trans. Inf. Forensics Secur. 19, 6693\u20136708 (2024)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"5091_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.sysarc.2024.103088","volume":"148","author":"A Yazdinejad","year":"2024","unstructured":"Yazdinejad, A., Dehghantanha, A., Srivastava, G., Karimipour, H., Parizi, R.M.: Hybrid privacy preserving federated learning against irregular users in next-generation internet of things. J. Syst. Architect. 148, 103088 (2024)","journal-title":"J. Syst. Architect."}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-024-05091-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-024-05091-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-024-05091-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T20:21:58Z","timestamp":1756930918000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-024-05091-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,16]]},"references-count":37,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["5091"],"URL":"https:\/\/doi.org\/10.1007\/s10586-024-05091-1","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"value":"1386-7857","type":"print"},{"value":"1573-7543","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,16]]},"assertion":[{"value":"7 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 November 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 December 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 June 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 declare no Conflict of interest or Conflict of interest related to this research. The research was conducted without any financial or personal relationships that could be perceived as influencing the work presented.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"403"}}