{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,21]],"date-time":"2025-05-21T10:09:22Z","timestamp":1747822162375,"version":"3.38.0"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T00:00:00Z","timestamp":1741046400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T00:00:00Z","timestamp":1741046400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Intell Syst"],"DOI":"10.1007\/s44196-025-00751-5","type":"journal-article","created":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T10:58:20Z","timestamp":1741085900000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Hybrid-CID: Securing IoT with Mongoose Optimization"],"prefix":"10.1007","volume":"18","author":[{"given":"S. Merlin","family":"Sheeba","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"R. S.","family":"Shaji","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,4]]},"reference":[{"key":"751_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2022.107810","volume":"99","author":"T Saba","year":"2022","unstructured":"Saba, T., Rehman, A., Sadad, T., Kolivand, H., Bahaj, S.A.: Anomaly-based intrusion detection system for IoT networks through deep learning model. Comput. Electr. Eng. 99, 107810 (2022). https:\/\/doi.org\/10.1016\/j.compeleceng.2022.107810","journal-title":"Comput. Electr. Eng."},{"key":"751_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2023.108626","volume":"107","author":"B Sharma","year":"2023","unstructured":"Sharma, B., Sharma, L., Lal, C., Roy, S.: Anomaly based network intrusion detection for IoT attacks using deep learning technique. Comput. Electr. Eng. 107, 108626 (2023). https:\/\/doi.org\/10.1016\/j.compeleceng.2023.108626","journal-title":"Comput. Electr. Eng."},{"issue":"2","key":"751_CR3","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/s10515-021-00298-7","volume":"28","author":"D Selvapandian","year":"2021","unstructured":"Selvapandian, D., Santhosh, R.: Deep learning approach for intrusion detection in IoT-multi cloud environment. Autom. Softw. Eng. 28(2), 19 (2021). https:\/\/doi.org\/10.1007\/s10515-021-00298-7","journal-title":"Autom. Softw. Eng."},{"issue":"02","key":"751_CR4","first-page":"48","volume":"02","author":"A Biju","year":"2024","unstructured":"Biju, A., Wilfred, F.S.: Network intrusion detection system with an edge based hybrid feature selection approach. Int. J. Syst. Design Comput. 02(02), 48\u201355 (2024)","journal-title":"Int. J. Syst. Design Comput."},{"issue":"3","key":"751_CR5","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1049\/cit2.12003","volume":"6","author":"ASR Wani","year":"2021","unstructured":"Wani, A.S.R., Khaliq, R.: SDN-based intrusion detection system for IoT using deep learning classifier (IDSIoT-SDL). CAAI Trans. Intell. Technol. 6(3), 281\u2013290 (2021). https:\/\/doi.org\/10.1049\/cit2.12003","journal-title":"CAAI Trans. Intell. Technol."},{"key":"751_CR6","doi-asserted-by":"publisher","unstructured":"Manivannan, I.S., Roobert, A.A., Muthukumaran, N.: A Detailed Analysis of Cloud Storage and Key Management Techniques in IoT Driven Smart Grids. In\u00a02024 7th International Conference on Circuit Power and Computing Technologies (ICCPCT), vol. 1, 1786\u20131791 (2024). IEEE. https:\/\/doi.org\/10.1109\/ICCPCT61902.2024.10672962","DOI":"10.1109\/ICCPCT61902.2024.10672962"},{"key":"751_CR7","doi-asserted-by":"publisher","first-page":"123448","DOI":"10.1109\/access.2021.3109081","volume":"9","author":"A Fatani","year":"2021","unstructured":"Fatani, A., Abd, E.M., Dahou, A., Al-Qaness, M.A., Lu, S.: IoT intrusion detection system using deep learning and enhanced transient search optimization. IEEE Access 9, 123448\u2013123464 (2021). https:\/\/doi.org\/10.1109\/access.2021.3109081","journal-title":"IEEE Access"},{"issue":"12","key":"751_CR8","doi-asserted-by":"publisher","first-page":"9395","DOI":"10.1016\/j.aej.2022.02.063","volume":"61","author":"YK Saheed","year":"2022","unstructured":"Saheed, Y.K., Abiodun, A.I., Misra, S., Holone, M.K., Colomo-Palacios, R.: A machine learning-based intrusion detection for detecting internet of things network attacks. Alexandria Eng. J. 61(12), 9395\u20139409 (2022). https:\/\/doi.org\/10.1016\/j.aej.2022.02.063","journal-title":"Alexandria Eng. J."},{"issue":"1","key":"751_CR9","doi-asserted-by":"publisher","DOI":"10.1002\/nem.2228","volume":"34","author":"V Rajyalakshmi","year":"2024","unstructured":"Rajyalakshmi, V., Lakshmanna, K.: Detection of car parking space by using Hybrid Deep DenseNet Optimization algorithm. Int. J. Netw. Manage 34(1), e2228 (2024). https:\/\/doi.org\/10.1002\/nem.2228","journal-title":"Int. J. Netw. Manage"},{"key":"751_CR10","doi-asserted-by":"publisher","unstructured":"Hema, M. (2024). HIDE-6G: Advanced Intrusion Detection System for Secure 6G Network using Deep Learning.\u00a0International Journal of Intelligent Engineering & Systems,\u00a017(5). https:\/\/doi.org\/10.22266\/ijies2024.1031.37","DOI":"10.22266\/ijies2024.1031.37"},{"key":"751_CR11","doi-asserted-by":"publisher","unstructured":"Jesi, M., Appathurai, A., Narayanaperumal, M., Kumar, A.: Load balancing in cloud computing via mayfly optimization algorithm.\u00a0Revue Roumaine Des Sciences Techniques\u2014S\u00e9rie \u00c9lectrotechnique Et \u00c9nerg\u00e9tique,\u00a069(1), 79\u201384 (2024). https:\/\/doi.org\/10.59277\/RRST-EE.2024.1.14","DOI":"10.59277\/RRST-EE.2024.1.14"},{"key":"751_CR12","unstructured":"Kumarraja Andanapalli, Suresh Kumar M.: Dynamic Power Allocation in Iot-Cloud Environment for Healthcare Applications. Int. J. Syst. Design Comput., 02(02), 39\u201347 (2024)."},{"key":"751_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2022.108190","volume":"102","author":"J Simon","year":"2022","unstructured":"Simon, J., Kapileswar, N., Polasi, P.K., Elaveini, M.A.: Hybrid intrusion detection system for wireless IoT networks using deep learning algorithm. Comput. Electr. Eng. 102, 108190 (2022). https:\/\/doi.org\/10.1016\/j.compeleceng.2022.108190","journal-title":"Comput. Electr. Eng."},{"issue":"3","key":"751_CR14","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1007\/s12046-023-02188-y","volume":"48","author":"S Senthil","year":"2023","unstructured":"Senthil, S., Muthukumaran, N.: Joined Bi-model RNN with spatial attention and GAN based IoT botnet attacks detection. S\u0101dhan\u0101 48(3), 141 (2023). https:\/\/doi.org\/10.1007\/s12046-023-02188-y","journal-title":"S\u0101dhan\u0101"},{"issue":"18","key":"751_CR15","doi-asserted-by":"publisher","first-page":"15175","DOI":"10.1007\/s00521-021-06826-6","volume":"34","author":"A Basati","year":"2022","unstructured":"Basati, A., Faghih, M.M.: DFE: efficient IoT network intrusion detection using deep feature extraction. Neural Comput. Appl. 34(18), 15175\u201315195 (2022). https:\/\/doi.org\/10.1007\/s00521-021-06826-6","journal-title":"Neural Comput. Appl."},{"key":"751_CR16","doi-asserted-by":"publisher","unstructured":"Liu Z., Thapa N., Shaver A., Roy K., Yuan X., Khorsandroo S.: Anomaly detection on iot network intrusion using machine learning. In 2020 International conference on artificial intelligence, big data, computing and data communication systems (icABCD) IEEE, 1\u20135 (2020). https:\/\/doi.org\/10.1109\/icabcd49160.2020.9183842","DOI":"10.1109\/icabcd49160.2020.9183842"},{"key":"751_CR17","doi-asserted-by":"publisher","unstructured":"Madhu B., Chari MVG., Vankdothu R., Silivery AK., Aerranagula V.: Intrusion detection models for IOT networks via deep learning approaches. Measurement: Sens. 25, 100641 (2023). https:\/\/doi.org\/10.1016\/j.measen.2022.100641","DOI":"10.1016\/j.measen.2022.100641"},{"key":"751_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2023.103560","volume":"136","author":"UK Lilhore","year":"2024","unstructured":"Lilhore, U.K., Dalal, S., Simaiya, S.: A cognitive security framework for detecting intrusions in IoT and 5G utilizing deep learning. Comput. Secur. 136, 103560 (2024). https:\/\/doi.org\/10.1016\/j.cose.2023.103560","journal-title":"Comput. Secur."},{"key":"751_CR19","doi-asserted-by":"publisher","unstructured":"Zhang., Hao., Yongdan Li., Zhihan Lv., Arun Kumar Sangaiah., Tao Huang.: A real-time and ubiquitous network attack detection based on deep belief network and support vector machine. IEEE\/CAA Journal of Automatica Sinica, 7(3), 790\u2013799 (2020). https:\/\/doi.org\/10.1109\/jas.2020.1003099","DOI":"10.1109\/jas.2020.1003099"},{"key":"751_CR20","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). https:\/\/doi.org\/10.1016\/j.ins.2019.10.069","journal-title":"Inf. Sci."},{"key":"751_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.adhoc.2021.102685","volume":"123","author":"P Sharma","year":"2021","unstructured":"Sharma, P., Jain, S., Gupta, S., Chamola, V.: Role of machine learning and deep learning in securing 5G-driven industrial IoT applications. Ad Hoc Netw. 123, 102685 (2021). https:\/\/doi.org\/10.1016\/j.adhoc.2021.102685","journal-title":"Ad Hoc Netw."},{"key":"751_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2021.107408","volume":"158","author":"B Abdollahzadeh","year":"2021","unstructured":"Abdollahzadeh, B., Gharehchopogh, F.S., Mirjalili, S.: African vultures optimization algorithm: a new nature-inspired metaheuristic algorithm for global optimization problems. Comput. Ind. Eng. 158, 107408 (2021). https:\/\/doi.org\/10.1016\/j.cie.2021.107408","journal-title":"Comput. Ind. Eng."},{"issue":"1","key":"751_CR23","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1016\/j.cie.2021.107408","volume":"30","author":"A Kaushik","year":"2024","unstructured":"Kaushik, A., Al-Raweshidy, H.: A novel intrusion detection system for internet of things devices and data. Wireless Netw. 30(1), 285\u2013294 (2024). https:\/\/doi.org\/10.1016\/j.cie.2021.107408","journal-title":"Wireless Netw."},{"issue":"12","key":"751_CR24","doi-asserted-by":"publisher","first-page":"13241","DOI":"10.1007\/s11227-023-05197-0","volume":"79","author":"O Elnakib","year":"2023","unstructured":"Elnakib, O., Shaaban, E., Mahmoud, M., Emara, K.: EIDM: Deep learning model for IoT intrusion detection systems. J. Supercomput. 79(12), 13241\u201313261 (2023). https:\/\/doi.org\/10.1007\/s11227-023-05197-0","journal-title":"J. Supercomput."},{"key":"751_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110941","volume":"279","author":"R Lazzarini","year":"2023","unstructured":"Lazzarini, R., Tianfield, H., Charissis, V.: A stacking ensemble of deep learning models for IoT intrusion detection. Knowl.-Based Syst. 279, 110941 (2023). https:\/\/doi.org\/10.1016\/j.knosys.2023.110941","journal-title":"Knowl.-Based Syst."},{"key":"751_CR26","doi-asserted-by":"publisher","DOI":"10.3389\/fcomp.2023.997159","volume":"5","author":"YK Saheed","year":"2023","unstructured":"Saheed, Y.K., Usman, A.A., Sukat, F.D., Abdulrahman, M.: A novel hybrid autoencoder and modified particle swarm optimization feature selection for intrusion detection in the internet of things network. Front. Comput. Sci. 5, 997159 (2023)","journal-title":"Front. Comput. Sci."},{"issue":"3","key":"751_CR27","doi-asserted-by":"publisher","first-page":"1557","DOI":"10.1007\/s10207-023-00803-x","volume":"23","author":"YK Saheed","year":"2024","unstructured":"Saheed, Y.K., Misra, S.: A voting gray wolf optimizer-based ensemble learning models for intrusion detection in the internet of things. Int. J. Inf. Secur. 23(3), 1557\u20131581 (2024)","journal-title":"Int. J. Inf. Secur."},{"issue":"1","key":"751_CR28","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1007\/s11276-023-03495-2","volume":"30","author":"OH Abdulganiyu","year":"2024","unstructured":"Abdulganiyu, O.H., Tchakoucht, T.A., Saheed, Y.K.: Towards an efficient model for network intrusion detection system (IDS): systematic literature review. Wireless Netw. 30(1), 453\u2013482 (2024)","journal-title":"Wireless Netw."}],"container-title":["International Journal of Computational Intelligence Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-025-00751-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44196-025-00751-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-025-00751-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T10:58:24Z","timestamp":1741085904000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44196-025-00751-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,4]]},"references-count":28,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["751"],"URL":"https:\/\/doi.org\/10.1007\/s44196-025-00751-5","relation":{},"ISSN":["1875-6883"],"issn-type":[{"value":"1875-6883","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,4]]},"assertion":[{"value":"6 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 March 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This paer has no conflict of interest for publishing","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}},{"value":"My research guide reviewed and ethically approved this manuscript for publishing in this journal.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"This article does not contain any studies with human or animal subjects performed by any of the authors.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human and Animal Rights"}},{"value":"I certify that I have explained the nature and purpose of this study to the above-named individual, and I have discussed the potential benefits of this study participation. The questions the individual had about this study have been answered, and we will always be available to address future questions.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}],"article-number":"48"}}