{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T04:18:54Z","timestamp":1758169134804,"version":"3.44.0"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2025,8,30]],"date-time":"2025-08-30T00:00:00Z","timestamp":1756512000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,30]],"date-time":"2025-08-30T00:00:00Z","timestamp":1756512000000},"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":[[2025,10]]},"DOI":"10.1007\/s10586-025-05240-0","type":"journal-article","created":{"date-parts":[[2025,8,30]],"date-time":"2025-08-30T10:41:55Z","timestamp":1756550515000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Optimization of recurrent neural networks for high-performance intrusion detection in network traffic"],"prefix":"10.1007","volume":"28","author":[{"given":"Maira","family":"Khalid","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmed Raza","family":"Mohsin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jehad","family":"Ali","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Byeong-hee","family":"Roh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,8,30]]},"reference":[{"key":"5240_CR1","unstructured":"Tiwari, A., Waoo, A.A.: Iot based smart home cyber-attack detection and defense. TIJER-International Research Journal 10(8) (2023)"},{"issue":"4","key":"5240_CR2","first-page":"16","volume":"85","author":"K Raghavan","year":"2020","unstructured":"Raghavan, K., Desai, M., Rajkumar, P.: Multi-step operations strategic framework for ransomware protection. SAM Advanced Management Journal 85(4), 16\u20132 (2020)","journal-title":"SAM Advanced Management Journal"},{"issue":"1","key":"5240_CR3","first-page":"9","volume":"2","author":"K Raghavan","year":"2017","unstructured":"Raghavan, K., Desai, M.S., Rajkumar, P.: Managing cybersecurity and ecommerce risks in small businesses. J. Manag. Sci. Bus. Intell. 2(1), 9\u201315 (2017)","journal-title":"J. Manag. Sci. Bus. Intell."},{"issue":"3","key":"5240_CR4","first-page":"465","volume":"17","author":"P Rajkumar","year":"2018","unstructured":"Rajkumar, P., Sandhu, R.: Safety decidability for pre-authorization usage control with identifier attribute domains. IEEE Transac. Depend. Secure Comput. 17(3), 465\u2013478 (2018)","journal-title":"IEEE Transac. Depend. Secure Comput."},{"issue":"5","key":"5240_CR5","doi-asserted-by":"publisher","first-page":"582","DOI":"10.1109\/TDSC.2015.2427834","volume":"13","author":"P Rajkumar","year":"2015","unstructured":"Rajkumar, P., Sandhu, R.: Safety decidability for pre-authorization usage control with finite attribute domains. IEEE Transac. Dependable Secure Comput. 13(5), 582\u2013590 (2015)","journal-title":"IEEE Transac. Dependable Secure Comput."},{"key":"5240_CR6","doi-asserted-by":"crossref","unstructured":"PV, R., Sandhu, R.: Poster: Security enhanced administrative role based access control models, 1802\u20131804 (2016)","DOI":"10.1145\/2976749.2989068"},{"issue":"3","key":"5240_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2024.102006","volume":"36","author":"C Jisi","year":"2024","unstructured":"Jisi, C., Roh, B.-H., Ali, J.: Reliable paths prediction with intelligent data plane monitoring enabled reinforcement learning in sd-iot. J. King Saud Univer. Comput. Inform. Sci. 36(3), 102006 (2024)","journal-title":"J. King Saud Univer. Comput. Inform. Sci."},{"key":"5240_CR8","doi-asserted-by":"crossref","unstructured":"Kizza, J.M.: System intrusion detection and prevention, 295\u2013323 (2024)","DOI":"10.1007\/978-3-031-47549-8_13"},{"key":"5240_CR9","doi-asserted-by":"publisher","first-page":"7550","DOI":"10.1109\/ACCESS.2020.3048198","volume":"9","author":"L Liu","year":"2020","unstructured":"Liu, L., Wang, P., Lin, J., Liu, L.: Intrusion detection of imbalanced network traffic based on machine learning and deep learning. IEEE access 9, 7550\u20137563 (2020)","journal-title":"IEEE access"},{"issue":"4","key":"5240_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3448974","volume":"54","author":"VS Lalapura","year":"2021","unstructured":"Lalapura, V.S., Amudha, J., Satheesh, H.S.: Recurrent neural networks for edge intelligence: a survey. ACM Comput. Surv. (CSUR) 54(4), 1\u201338 (2021)","journal-title":"ACM Comput. Surv. (CSUR)"},{"issue":"1","key":"5240_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s42400-019-0038-7","volume":"2","author":"A Khraisat","year":"2019","unstructured":"Khraisat, A., Gondal, I., Vamplew, P., Kamruzzaman, J.: Survey of intrusion detection systems: techniques, datasets and challenges. Cybersecurity 2(1), 1\u201322 (2019)","journal-title":"Cybersecurity"},{"issue":"5","key":"5240_CR12","doi-asserted-by":"publisher","first-page":"834","DOI":"10.3390\/pr9050834","volume":"9","author":"MA Khan","year":"2021","unstructured":"Khan, M.A.: Hcrnnids: Hybrid convolutional recurrent neural network-based network intrusion detection system. Processes 9(5), 834 (2021)","journal-title":"Processes"},{"key":"5240_CR13","doi-asserted-by":"crossref","unstructured":"Chaibi, N., Atmani, B., Mokaddem, M.: Deep learning approaches to intrusion detection: A new performance of ann and rnn on nsl-kdd, 45\u201349 (2020)","DOI":"10.1145\/3432867.3432889"},{"key":"5240_CR14","doi-asserted-by":"publisher","first-page":"1552","DOI":"10.7717\/peerj-cs.1552","volume":"9","author":"Q Abbas","year":"2023","unstructured":"Abbas, Q., Hina, S., Sajjad, H., Zaidi, K.S., Akbar, R.: Optimization of predictive performance of intrusion detection system using hybrid ensemble model for secure systems. PeerJ Comput. Sci. 9, 1552 (2023)","journal-title":"PeerJ Comput. Sci."},{"key":"5240_CR15","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/j.comcom.2022.12.010","volume":"199","author":"SM Kasongo","year":"2023","unstructured":"Kasongo, S.M.: A deep learning technique for intrusion detection system using a recurrent neural networks based framework. Comput. Commun. 199, 113\u2013125 (2023)","journal-title":"Comput. Commun."},{"key":"5240_CR16","doi-asserted-by":"publisher","first-page":"29575","DOI":"10.1109\/ACCESS.2020.2972627","volume":"8","author":"T Su","year":"2020","unstructured":"Su, T., Sun, H., Zhu, J., Wang, S., Li, Y.: Bat: Deep learning methods on network intrusion detection using nsl-kdd dataset. IEEE Access 8, 29575\u201329585 (2020)","journal-title":"IEEE Access"},{"key":"5240_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.physd.2019.132306","volume":"404","author":"A Sherstinsky","year":"2020","unstructured":"Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D Nonlinear Phenom. 404, 132306 (2020)","journal-title":"Physica D Nonlinear Phenom."},{"key":"5240_CR18","doi-asserted-by":"crossref","unstructured":"Idrissi, I., Mostafa\u00a0Azizi, M., Moussaoui, O.: A lightweight optimized deep learning-based host-intrusion detection system deployed on the edge for iot. International Journal of Computing and Digital System (2021)","DOI":"10.12785\/ijcds\/110117"},{"key":"5240_CR19","doi-asserted-by":"crossref","unstructured":"Raiaan, M.A.K., Sakib, S., Fahad, N.M., Al\u00a0Mamun, A., Rahman, M.A., Shatabda, S., Mukta, M.S.H.: A systematic review of hyperparameter optimization techniques in convolutional neural networks. Decision Analytics Journal, 100470 (2024)","DOI":"10.1016\/j.dajour.2024.100470"},{"key":"5240_CR20","doi-asserted-by":"crossref","unstructured":"Meseguer\u00a0Llopis, J., Pieczerak, J., Janaszka, T.: Minimizing latency of critical traffic through sdn, 1\u20136 (2016)","DOI":"10.1109\/NAS.2016.7549408"},{"key":"5240_CR21","unstructured":"Panigrahi, R., Borah, S.: A detailed analysis of cicids2017 dataset for designing intrusion detection systems. International Journal of Engineering & Technology 7(3.24), 479\u2013482 (2018)"},{"key":"5240_CR22","doi-asserted-by":"publisher","first-page":"842","DOI":"10.1016\/j.procs.2015.07.490","volume":"57","author":"P Aggarwal","year":"2015","unstructured":"Aggarwal, P., Sharma, S.K.: Analysis of kdd dataset attributes-class wise for intrusion detection. Procedia Comput. Sci. 57, 842\u2013851 (2015)","journal-title":"Procedia Comput. Sci."},{"key":"5240_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.simpat.2019.102031","volume":"101","author":"M Almiani","year":"2020","unstructured":"Almiani, M., AbuGhazleh, A., Al-Rahayfeh, A., Atiewi, S., Razaque, A.: Deep recurrent neural network for iot intrusion detection system. Simulat. Modell. Pract. Theory 101, 102031 (2020)","journal-title":"Simulat. Modell. Pract. Theory"},{"key":"5240_CR24","doi-asserted-by":"crossref","unstructured":"Chougule, A., Kulkarni, I., Alladi, T., Chamola, V., Yu, F.R.: Hybridsecnet: In-vehicle security on controller area networks through a hybrid two-step lstm-cnn model. IEEE Transactions on Vehicular Technology (2024)","DOI":"10.1109\/TVT.2024.3413849"},{"issue":"11","key":"5240_CR25","doi-asserted-by":"publisher","first-page":"13068","DOI":"10.1109\/TITS.2022.3201548","volume":"24","author":"A Chougule","year":"2022","unstructured":"Chougule, A., Kohli, V., Chamola, V., Yu, F.R.: Multibranch reconstruction error (mbre) intrusion detection architecture for intelligent edge-based policing in vehicular ad-hoc networks. IEEE Transac. Intell. Transport. Sys. 24(11), 13068\u201313077 (2022)","journal-title":"IEEE Transac. Intell. Transport. Sys."},{"issue":"3","key":"5240_CR26","doi-asserted-by":"publisher","first-page":"4815","DOI":"10.1109\/JIOT.2018.2871719","volume":"6","author":"N Moustafa","year":"2018","unstructured":"Moustafa, N., Turnbull, B., Choo, K.-K.R.: An ensemble intrusion detection technique based on proposed statistical flow features for protecting network traffic of internet of things. IEEE Internet Thing J. 6(3), 4815\u20134830 (2018)","journal-title":"IEEE Internet Thing J."},{"key":"5240_CR27","doi-asserted-by":"publisher","first-page":"6430","DOI":"10.1109\/ACCESS.2021.3140015","volume":"10","author":"N Abdalgawad","year":"2021","unstructured":"Abdalgawad, N., Sajun, A., Kaddoura, Y., Zualkernan, I.A., Aloul, F.: Generative deep learning to detect cyberattacks for the iot-23 dataset. IEEE Access 10, 6430\u20136441 (2021)","journal-title":"IEEE Access"},{"key":"5240_CR28","doi-asserted-by":"publisher","first-page":"103906","DOI":"10.1109\/ACCESS.2021.3094024","volume":"9","author":"I Ullah","year":"2021","unstructured":"Ullah, I., Mahmoud, Q.H.: Design and development of a deep learning-based model for anomaly detection in iot networks. IEEE Access 9, 103906\u2013103926 (2021)","journal-title":"IEEE Access"},{"key":"5240_CR29","doi-asserted-by":"crossref","unstructured":"Ullah, I., Mahmoud, Q.H.: An anomaly detection model for iot networks based on flow and flag features using a feed-forward neural network, 363\u2013368 (2022). IEEE","DOI":"10.1109\/CCNC49033.2022.9700597"},{"issue":"4","key":"5240_CR30","doi-asserted-by":"publisher","first-page":"893","DOI":"10.1007\/s10207-023-00663-5","volume":"22","author":"B Bowen","year":"2023","unstructured":"Bowen, B., Chennamaneni, A., Goulart, A., Lin, D.: Blocnet: a hybrid, dataset-independent intrusion detection system using deep learning. Int. J. Inf. Secur. 22(4), 893\u2013917 (2023)","journal-title":"Int. J. Inf. Secur."},{"key":"5240_CR31","doi-asserted-by":"crossref","unstructured":"Chaibi, N., Atmani, B., Mokaddem, M.: Deep learning approaches to intrusion detection: A new performance of ann and rnn on nsl-kdd, 45\u201349 (2020)","DOI":"10.1145\/3432867.3432889"},{"issue":"4","key":"5240_CR32","doi-asserted-by":"publisher","first-page":"893","DOI":"10.1007\/s10207-023-00663-5","volume":"22","author":"B Bowen","year":"2023","unstructured":"Bowen, B., Chennamaneni, A., Goulart, A., Lin, D.: Blocnet: a hybrid, dataset-independent intrusion detection system using deep learning. Int. J. Inf. Secur. 22(4), 893\u2013917 (2023)","journal-title":"Int. J. Inf. Secur."},{"key":"5240_CR33","doi-asserted-by":"publisher","first-page":"104225","DOI":"10.1016\/j.trc.2023.104225","volume":"153","author":"Q Xu","year":"2023","unstructured":"Xu, Q., Pang, Y., Liu, Y.: Air traffic density prediction using bayesian ensemble graph attention network (began). Transport. Res. Part C Emerg. Technol. 153, 104225 (2023)","journal-title":"Transport. Res. Part C Emerg. Technol."},{"key":"5240_CR34","doi-asserted-by":"crossref","unstructured":"Xu, Q., Pang, Y., Zhou, X., Liu, Y.: Pigat: Physics-informed graph attention transformer for air traffic state prediction. IEEE Transactions on Intelligent Transportation Systems (2024)","DOI":"10.1109\/TITS.2024.3386128"}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-025-05240-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-025-05240-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-025-05240-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T21:24:01Z","timestamp":1758144241000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-025-05240-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,30]]},"references-count":34,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["5240"],"URL":"https:\/\/doi.org\/10.1007\/s10586-025-05240-0","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"type":"print","value":"1386-7857"},{"type":"electronic","value":"1573-7543"}],"subject":[],"published":{"date-parts":[[2025,8,30]]},"assertion":[{"value":"26 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 February 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 March 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 August 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.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"563"}}