{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T15:25:53Z","timestamp":1773933953582,"version":"3.50.1"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T00:00:00Z","timestamp":1766966400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T00:00:00Z","timestamp":1766966400000},"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":[[2026,2]]},"DOI":"10.1007\/s10586-025-05874-0","type":"journal-article","created":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T13:25:21Z","timestamp":1767014721000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Integrating hybrid deep learning architecture with enhanced feature selection techniques to mitigate the multiple attacks"],"prefix":"10.1007","volume":"29","author":[{"given":"M.","family":"Saritha","sequence":"first","affiliation":[]},{"given":"Saidireddy","family":"Malgireddy","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,12,29]]},"reference":[{"key":"5874_CR1","doi-asserted-by":"publisher","first-page":"848","DOI":"10.1007\/s43538-024-00372-0","volume":"91","author":"R Ji","year":"2025","unstructured":"Ji, R., Kumar, N., Padha, D.: CNN-GWO-voting & hybrid: Ensemble learning inspired intrusion detection approaches for cyber-physical systems. Proc. Indian Natl. Sci. Acad. 91, 848\u2013862 (2025). https:\/\/doi.org\/10.1007\/s43538-024-00372-0","journal-title":"Proc. Indian Natl. Sci. Acad."},{"key":"5874_CR2","doi-asserted-by":"publisher","DOI":"10.1007\/s10791-025-09708-w","author":"AK Shukla","year":"2025","unstructured":"Shukla, A.K., Dwivedi, S., Mishra, A.: An effective hybrid deep learning metaheuristic model for robust IoT intrusion detection. Discover Computing (2025). https:\/\/doi.org\/10.1007\/s10791-025-09708-w","journal-title":"Discover Computing"},{"issue":"30","key":"5874_CR3","doi-asserted-by":"publisher","first-page":"3069","DOI":"10.17485\/IJST\/v17i30.1794","volume":"17","author":"R Ji","year":"2024","unstructured":"Ji, R., Kumar, N., Padha, D.: Hybrid enhanced intrusion detection frameworks for Cyber-Physical systems via optimal features selection. Indian J. Sci. Technol. 17(30), 3069\u20133079 (2024). https:\/\/doi.org\/10.17485\/IJST\/v17i30.1794","journal-title":"Indian J. Sci. Technol."},{"key":"5874_CR4","doi-asserted-by":"publisher","first-page":"66432","DOI":"10.1109\/ACCESS.2024.3398007","volume":"12","author":"X Hu","year":"2024","unstructured":"Hu, X., Meng, X., Liu, S., Liang, L.: An Improved Algorithm for Network Intrusion Detection Based on Deep Residual Networks. IEEE Access 12, 66432\u201366441 (2024)","journal-title":"IEEE Access"},{"key":"5874_CR5","doi-asserted-by":"crossref","unstructured":"El-Rady, A.A., Osama, H., Sadik, R., El Badwy, H.: Network Intrusion Detection CNN Model for Realistic Network Attacks Based on Network Traffic Classification. In Proceedings of the 2023 40th National Radio Science Conference (NRSC), Giza, Egypt, 30 May\u20131 June ; Volume 1, pp. 167\u2013178. (2023)","DOI":"10.1109\/NRSC58893.2023.10152872"},{"key":"5874_CR6","doi-asserted-by":"publisher","first-page":"24808","DOI":"10.1109\/ACCESS.2023.3254915","volume":"11","author":"J Du","year":"2023","unstructured":"Du, J., Yang, K., Hu, Y., Jiang, L.: NIDS-CNNLSTM: Network intrusion detection classification model based on deep learning. IEEE Access 11, 24808\u201324821 (2023)","journal-title":"IEEE Access"},{"key":"5874_CR7","doi-asserted-by":"crossref","unstructured":"Salazar, A., Vargas, N., Safont, G., Vergara, L.: Late Fusion for Improving Intrusion Detection in a Network Traffic Dataset. In Proceedings of the 2021 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA, 15\u201317 December ; pp. 1684\u20131689. (2021)","DOI":"10.1109\/CSCI54926.2021.00320"},{"key":"5874_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113819","volume":"163","author":"A Salazar","year":"2021","unstructured":"Salazar, A., Vergara, L., Safont, G.: Generative Adversarial Networks and Markov Random Fields for oversampling very small training sets. Expert Syst. Appl. 163, 113819 (2021)","journal-title":"Expert Syst. Appl."},{"key":"5874_CR9","doi-asserted-by":"publisher","first-page":"5867","DOI":"10.1109\/TII.2020.3046566","volume":"17","author":"H Yi","year":"2020","unstructured":"Yi, H., Jiang, Q., Yan, X., Wang, B.: Imbalanced classification based on minority clustering Smote with wind turbine fault detection application. IEEE Trans. Ind. Inf. 17, 5867\u20135875 (2020)","journal-title":"IEEE Trans. Ind. Inf."},{"key":"5874_CR10","doi-asserted-by":"publisher","DOI":"10.3390\/buildings12091472","volume":"12","author":"MB Shishehgarkhaneh","year":"2022","unstructured":"Shishehgarkhaneh, M.B., Azizi, M., Basiri, M., Moehler, R.C.: BIM-based resource tradeoff in project scheduling using fire hawk optimizer (FHO). Buildings 12, 1472 (2022)","journal-title":"Buildings"},{"key":"5874_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2023.103784","volume":"221","author":"S Latif","year":"2024","unstructured":"Latif, S., Boulila, W., Koubaa, A., Zou, Z., Ahmad, J.: DTL-IDS: An optimized Intrusion Detection Framework using Deep Transfer Learning and Genetic Algorithm. Journal of Network and Computer Applications 221, 103784 (2024)","journal-title":"Journal of Network and Computer Applications"},{"key":"5874_CR12","doi-asserted-by":"publisher","first-page":"293","DOI":"10.3390\/electronics12020293","volume":"12","author":"J Figueiredo","year":"2023","unstructured":"Figueiredo, J., Serr\u00e3o, C., de Almeida, A.M.: Deep learning model transposition for network intrusion detection systems. Electronics. 12, 293 (2023)","journal-title":"Electronics"},{"key":"5874_CR13","doi-asserted-by":"publisher","first-page":"550","DOI":"10.3390\/s23010550","volume":"23","author":"YN Rao","year":"2023","unstructured":"Rao, Y.N., Suresh Babu, K.: An imbalanced generative adversarial network-based approach for network intrusion detection in an imbalanced dataset. Sensors. 23, 550 (2023)","journal-title":"Sensors"},{"key":"5874_CR14","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1007\/s43926-025-00157-x","volume":"5","author":"P Kalpana","year":"2025","unstructured":"Kalpana, P., Tappari, S., Smitha, L., et al.: A novel end-to-end privacy preserving deep Aquila feed forward networks on healthcare 4.0 environment. Discov Internet Things. 5, 65 (2025). https:\/\/doi.org\/10.1007\/s43926-025-00157-x","journal-title":"Discov Internet Things"},{"key":"5874_CR15","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1016\/j.microrel.2017.03.006","volume":"75","author":"Z Chen","year":"2017","unstructured":"Chen, Z., Chen, S., Chen, X., Li, C., Sanchez, R.V., Qin, H.: Deep neural networks-based rolling bearing fault diagnosis. Microelectron. Reliab. 75, 327\u2013333 (2017)","journal-title":"Microelectron. Reliab."},{"key":"5874_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2022.109974","volume":"187","author":"G Li","year":"2023","unstructured":"Li, G., Hu, J., Shan, D., Ao, J., Huang, B., Huang, Z.: A CNN model based on innovative expansion operation improving the fault diagnosis accuracy of drilling pump fluid end. Mechanical Systems and Signal Processing 187, 109974 (2023)","journal-title":"Mechanical Systems and Signal Processing"},{"key":"5874_CR17","doi-asserted-by":"publisher","unstructured":"Kalpana, P., Srilatha, P., Krishna, G.S., Alkhayyat, A., Mazumder, D., Denial of Service (DoS) Attack Detection Using Feed Forward Neural Network in Cloud Environment,: International Conference on Data Science and Network Security (ICDSNS), Tiptur, India, 2024, pp. 1\u20134, (2024). https:\/\/doi.org\/10.1109\/ICDSNS62112.2024.10691181","DOI":"10.1109\/ICDSNS62112.2024.10691181"},{"key":"5874_CR18","doi-asserted-by":"publisher","unstructured":"Aruna, E., Sahayadhas, et al.: Feb. A Web 3.0 Integrated Blockchain Enabled Access System Augmented by Meta-Heuristic Cognitive Learning Framework for Mitigating Threats in IoT Enabled Consumer Electronic Devices, in IEEE Transactions on Consumer Electronics, vol. 71, no. 1, pp. 1201\u20131210, (2025). https:\/\/doi.org\/10.1109\/TCE.2025.3553741","DOI":"10.1109\/TCE.2025.3553741"},{"key":"5874_CR19","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3180392","author":"X Li","year":"2023","unstructured":"X. Li, C. Lv, W. Wang, G. Li, L. Yang and J. Yang, \"Generalized Focal Loss: Towards Efficient Representation Learning for Dense Object Detection,\" in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 3, pp. 3139-3153, 1 March 2023, doi: 10.1109\/TPAMI.2022.3180392. https:\/\/doi.org\/10.1109\/TPAMI.2022.3180392","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5874_CR20","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-82566-6","volume":"15","author":"FAF Alrslani","year":"2025","unstructured":"Alrslani, F.A.F., Alohali, M.A., Aljebreen, M., Alqahtani, H., Alshuhail, A., Alshammeri, M., Almukadi, W.S.: Enhancing cybersecurity via attribute reduction with deep learning model for false data injection attack recognition. Scientific Reports 15, 3944 (2025). https:\/\/doi.org\/10.1038\/s41598-024-82566-6","journal-title":"Scientific Reports"},{"key":"5874_CR21","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-94445-9","volume":"15","author":"H Khan","year":"2025","unstructured":"Khan, H., Tejani, G.G., AlGhamdi, R., Alasmari, S., Sharma, N.K., Sharma, S.K.: A secure and efficient deep learning-based intrusion detection framework for the internet of vehicles. Scientific Reports 15, 12236 (2025). https:\/\/doi.org\/10.1038\/s41598-025-94445-9","journal-title":"Scientific Reports"},{"key":"5874_CR22","doi-asserted-by":"publisher","first-page":"580","DOI":"10.3390\/s25020580","volume":"25","author":"B Susilo","year":"2025","unstructured":"Susilo, B., Muis, A., Sari, R.F.: Intelligent intrusion detection system against variousattacksbasedona HybridDeepLearningAlgorithm. Sensors. 25, 580 (2025). https:\/\/doi.org\/10.3390\/s25020580","journal-title":"Sensors"},{"key":"5874_CR23","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-85547-5","volume":"15","author":"V Kandasamy","year":"2025","unstructured":"Kandasamy, V., Roseline, A.A.: Harnessing advanced hybrid deep learning model for real-time detection and prevention of man-in-the-middle cyber-attacks. Scientific Reports 15, 1697 (2025). https:\/\/doi.org\/10.1038\/s41598-025-85547-5","journal-title":"Scientific Reports"},{"key":"5874_CR24","first-page":"316","volume":"5","author":"W Ferdose Urmi","year":"2024","unstructured":"Ferdose Urmi, W., Uddin, M.N., Uddin, M.A., Talukder, M.A., Hasan, M.R., Paul, S., Chanda, M., Ayode, J., Khraisat, A., Hossen, R., Imran, F.: A stacked ensemble approach to detect cyberattacks based on feature selection techniques. Int. J. Cogn. Comput. Eng. 5, 316\u2013331 (2024). http:\/\/www.keaipublishing.com\/en\/journals\/international-journal-of-cognitive-computing-in-engineering\/","journal-title":"Int. J. Cogn. Comput. Eng."},{"key":"5874_CR25","doi-asserted-by":"publisher","first-page":"8505","DOI":"10.3390\/app14188505","volume":"14","author":"B Ta\u00b8 sc\u0131","year":"2024","unstructured":"Ta\u00b8 sc\u0131, B.: Deep-Learning based approach for IoT attack and malware detection. Appl. Sci. 14, 8505 (2024). https:\/\/doi.org\/10.3390\/app14188505","journal-title":"Appl. Sci."},{"key":"5874_CR26","doi-asserted-by":"publisher","first-page":"1678","DOI":"10.3390\/electronics13091678","volume":"13","author":"K Harahsheh","year":"2024","unstructured":"Harahsheh, K., Al-Naimat, R., Chen, C.-H.: Using feature selection enhancement to evaluate attack detection in the internet of things environment. Electronics. 13, 1678 (2024). https:\/\/doi.org\/10.3390\/electronics13091678","journal-title":"Electronics"},{"key":"5874_CR27","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-83682-z","volume":"14","author":"G Subramanian","year":"2024","unstructured":"Subramanian, G., Chinnadurai, M.: Hybrid quantum enhanced federated learning for cyber attack detection. Scientific Reports 14, 32038 (2024). https:\/\/doi.org\/10.1038\/s41598-024-83682-z","journal-title":"Scientific Reports"},{"key":"5874_CR28","doi-asserted-by":"publisher","first-page":"4002","DOI":"10.3390\/s24124002","volume":"24","author":"J Huang","year":"2024","unstructured":"Huang, J., Chen, Z., Liu, S.-Z., Zhang, H., Long, H.-X.: Improved intrusion detection based on hybrid deep learning models and federated learning. Sensors. 24, 4002 (2024). https:\/\/doi.org\/10.3390\/s24124002","journal-title":"Sensors"},{"key":"5874_CR29","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/8383461","volume":"2022","author":"A Rehman","year":"2022","unstructured":"Rehman, A., Dama\u0161evi\u010dius, R., Saba, T., Khan, M.Z., Bahaj, S.A.: Internet-of-things-based suspicious activity recognition using multimodalities of computer vision for smart city security. Security and Communication Networks 2022, 8383461 (2022). https:\/\/doi.org\/10.1155\/2022\/8383461","journal-title":"Security and Communication Networks"},{"key":"5874_CR30","unstructured":"https:\/\/www.kaggle.com\/datasets\/hassan06\/nslkdd"},{"key":"5874_CR31","unstructured":"https:\/\/www.kaggle.com\/datasets\/dhoogla\/unswnb15"},{"key":"5874_CR32","unstructured":"https:\/\/www.kaggle.com\/datasets\/chethuhn\/network-intrusion-dataset"},{"key":"5874_CR33","doi-asserted-by":"publisher","unstructured":"SaravanaKumar, G., Kalpana, P., Ponugoti, G.V., Murthy: Beetle-Optimized hybrid ensemble for Multi\u2010Attack classification in VANETs. Trans. Emerg. Telecommunications Technol. 36(10) (Oct. 2025). https:\/\/doi.org\/10.1002\/ett.70281","DOI":"10.1002\/ett.70281"},{"key":"5874_CR34","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1007\/s11063-022-10892-9","volume":"55","author":"S Karthic","year":"2023","unstructured":"Karthic, S., Kumar, S.M.: Hybrid optimized deep neural network with enhanced conditional random field based intrusion detection on wireless sensor network. Neural Process. Lett. 55, 459\u2013479 (2023). https:\/\/doi.org\/10.1007\/s11063-022-10892-9","journal-title":"Neural Process. Lett."},{"key":"5874_CR35","doi-asserted-by":"publisher","first-page":"809","DOI":"10.1007\/s42979-023-02311-0","volume":"4","author":"K Sundaram","year":"2023","unstructured":"Sundaram, K., Subramanian, S., Natarajan, Y., et al.: Improving performance of intrusion detection using ALO selected features and GRU network. SN COMPUT. SCI. 4, 809 (2023). https:\/\/doi.org\/10.1007\/s42979-023-02311-0","journal-title":"SN COMPUT. SCI."},{"key":"5874_CR36","doi-asserted-by":"publisher","unstructured":"Suhana, S., Karthic, S., Yuvaraj, N.: Ensemble based Dimensionality Reduction for Intrusion Detection using Random Forest in Wireless Networks, 5th International Conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, India, 2023, pp. 704\u2013708, (2023). https:\/\/doi.org\/10.1109\/ICSSIT55814.2023.10060929","DOI":"10.1109\/ICSSIT55814.2023.10060929"},{"key":"5874_CR37","doi-asserted-by":"publisher","DOI":"10.3390\/app14177763","volume":"14","author":"MH Alsulami","year":"2024","unstructured":"Alsulami, M.H.: Residual dense Optimization-Based Multi-Attention transformer to detect network intrusion against cyber attacks. Applied Sciences 14, 7763 (2024). https:\/\/doi.org\/10.3390\/app14177763","journal-title":"Applied Sciences"}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-025-05874-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-025-05874-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-025-05874-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T13:08:32Z","timestamp":1773925712000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-025-05874-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,29]]},"references-count":37,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,2]]}},"alternative-id":["5874"],"URL":"https:\/\/doi.org\/10.1007\/s10586-025-05874-0","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"value":"1386-7857","type":"print"},{"value":"1573-7543","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,29]]},"assertion":[{"value":"12 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 November 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 November 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 December 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"}},{"value":"This study involved no experiments on humans or animals by the author.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"68"}}