{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T04:30:23Z","timestamp":1792816223249,"version":"4.5.0"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T00:00:00Z","timestamp":1726444800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T00:00:00Z","timestamp":1726444800000},"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\/s44196-024-00649-8","type":"journal-article","created":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T16:03:35Z","timestamp":1726502615000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A Shark Inspired Ensemble Deep Learning Stacks for Ensuring the Security in Internet of Things (IoT)-Based Smart City Infrastructure"],"prefix":"10.1007","volume":"17","author":[{"given":"P. Jagadish","family":"Kumar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S.","family":"Neduncheliyan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,9,16]]},"reference":[{"key":"649_CR1","doi-asserted-by":"publisher","unstructured":"Saba, T. Intrusion detection in smart city hospitals using ensemble classifiers. In: 13th International Conference on the Developments in eSystems Engineering (DeSE), Liverpool, UK (2020). 14\u201317. https:\/\/doi.org\/10.1109\/DeSE51703.2020.9450247","DOI":"10.1109\/DeSE51703.2020.9450247"},{"key":"649_CR2","doi-asserted-by":"publisher","first-page":"79056","DOI":"10.1109\/ACCESS.2023.3298559","volume":"11","author":"TN Qureshi","year":"2023","unstructured":"Qureshi, T.N., Khan, Z.A., Javaid, N., Aldegheishem, A., Rasheed, M.B., Alrajeh, N.: Elephant herding robustness evolution algorithm with multi-clan co-evolution against cyber attacks for scale-free internet of things in smart cities. IEEE Access 11, 79056\u201379072 (2023). https:\/\/doi.org\/10.1109\/ACCESS.2023.3298559","journal-title":"IEEE Access"},{"key":"649_CR3","doi-asserted-by":"publisher","first-page":"6031","DOI":"10.3390\/su15076031","volume":"15","author":"D Prabakar","year":"2023","unstructured":"Prabakar, D., Sundarrajan, M., Manikandan, R., Jhanjhi, N.Z., Masud, M., Alqhatani, A.: Energy analysis-based cyber attack detection by IoT with artificial intelligence in a sustainable smart city. Sustainability 15, 6031 (2023). https:\/\/doi.org\/10.3390\/su15076031","journal-title":"Sustainability"},{"key":"649_CR4","unstructured":"Symantec. Internet Security Threat Report; Symantec: Tempe, AZ, USA, 2020, 1."},{"key":"649_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3486221","volume":"55","author":"B Costa","year":"2022","unstructured":"Costa, B., Bachiega, J., de Carvalho, L.R., Araujo, A.P.F.: Orchestration in fog computing: a comprehensive survey. ACM Comput. Surv.Comput. Surv. 55, 1\u201334 (2022). https:\/\/doi.org\/10.1145\/3486221","journal-title":"ACM Comput. Surv.Comput. Surv."},{"key":"649_CR6","doi-asserted-by":"publisher","unstructured":"Chang, V., Golightly, L., Modesti, P., Xu, Q.A., Doan, L.M.T., Hall, K., Boddu, S., Kobusi \u00b4nska, A. A survey on intrusion detection systems for fog and cloud computing. Future Internet 2022, 14, 89. https:\/\/doi.org\/10.3390\/fi14030089","DOI":"10.3390\/fi14030089"},{"key":"649_CR7","doi-asserted-by":"publisher","first-page":"107810","DOI":"10.1109\/ICAC3N53548.2021.9725494","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.. Electr. Eng. 99, 107810 (2022). https:\/\/doi.org\/10.1109\/ICAC3N53548.2021.9725494","journal-title":"Comput. Electr. Eng.. Electr. Eng."},{"key":"649_CR8","doi-asserted-by":"publisher","first-page":"407","DOI":"10.3390\/jmse11020407","volume":"11","author":"Z Yan","year":"2023","unstructured":"Yan, Z., Yang, G., He, R., Yang, H., Ci, H., Wang, R.: Ship trajectory clustering based on trajectory resampling and enhanced BIRCH algorithm. J. Mar. Sci. Eng. 11, 407 (2023). https:\/\/doi.org\/10.3390\/jmse11020407","journal-title":"J. Mar. Sci. Eng."},{"key":"649_CR9","doi-asserted-by":"publisher","unstructured":"Kumar, R., Kumar, P., Aljuhani, A., Islam, A. K. M. N., Jolfaei, A., Garg, S.: Deep Learning and Smart Contract-Assisted Secure Data Sharing for IoT-Based Intelligent Agriculture, in IEEE Intelligent Systems. 38(4), 42\u201351 (2023). https:\/\/doi.org\/10.1109\/MIS.2022.3201553.","DOI":"10.1109\/MIS.2022.3201553"},{"key":"649_CR10","doi-asserted-by":"publisher","unstructured":"Li, J., Li, Y., Ding, C., Yu, J., Ren, Y.: Identity-based Secure and Efficient Intelligent Inference Framework for IoT-Cloud System. In: 2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP), Beijing, China, 2022, 1-6, https:\/\/doi.org\/10.1109\/PAAP56126.2022.10010411","DOI":"10.1109\/PAAP56126.2022.10010411"},{"issue":"5","key":"649_CR11","doi-asserted-by":"publisher","first-page":"2051","DOI":"10.18280\/ts.400523","volume":"40","author":"P Kalpana","year":"2023","unstructured":"Kalpana, P., Anandan, R.: A capsule attention network for plant disease classification. Traitement du Signal 40(5), 2051\u20132062 (2023). https:\/\/doi.org\/10.18280\/ts.400523","journal-title":"Traitement du Signal"},{"key":"649_CR12","doi-asserted-by":"publisher","unstructured":"Syed M.T.N.: Intelligent intrusion detection framework for\u00a0multi-clouds \u2013 IoT environment using swarm-based deep learning classifier. J Cloud Comput Adv Syst Appl. 12, 134 (2023). https:\/\/doi.org\/10.1186\/s13677-023-00509-4","DOI":"10.1186\/s13677-023-00509-4"},{"key":"649_CR13","doi-asserted-by":"publisher","first-page":"8660","DOI":"10.1038\/s41598-024-56393-8","volume":"14","author":"P Kalpana","year":"2024","unstructured":"Kalpana, P., Anandan, R., Hussien, A.G., et al.: Plant disease recognition using residual convolutional enlightened Swin transformer networks. Sci. Rep. 14, 8660 (2024). https:\/\/doi.org\/10.1038\/s41598-024-56393-8","journal-title":"Sci. Rep."},{"key":"649_CR14","doi-asserted-by":"publisher","unstructured":"Said Elsayed, M., Le-Khac, N.A., Dev, S., Jurcut, A.D.: Network anomaly detection using LSTM based autoencoder. In: Proceedings of the 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks 2020, Alicante, Spain, 16\u201320. https:\/\/doi.org\/10.3390\/s24092833","DOI":"10.3390\/s24092833"},{"key":"649_CR15","doi-asserted-by":"publisher","first-page":"279","DOI":"10.3390\/s22218417","volume":"11","author":"B Susilo","year":"2020","unstructured":"Susilo, B., Sari, R.F.: Intrusion detection in IoT networks using deep learning algorithm. Information 11, 279 (2020). https:\/\/doi.org\/10.3390\/s22218417","journal-title":"Information"},{"key":"649_CR16","doi-asserted-by":"crossref","unstructured":"Elsayed, M.S., Jahromi, H.Z., Nazir, M.M., Jurcut, A.D.: The role of CNN for intrusion detection systems: An improved CNN learning approach for SDNs. In: Future Access Enablers for Ubiquitous and Intelligent Infrastructures, Proceedings of the 5th EAI International Conference, FABULOUS 2021, Virtual Event, 6\u20137 May 2021; Springer: Cham, Switzerland, 2021; 91\u2013104.","DOI":"10.1007\/978-3-030-78459-1_7"},{"key":"649_CR17","doi-asserted-by":"publisher","first-page":"74571","DOI":"10.1109\/ACCESS.2020.2988854","volume":"8","author":"A Samy","year":"2020","unstructured":"Samy, A., Yu, H., Zhang, H.: Fog-based attack detection framework for internet of things using deep learning. IEEE Access 8, 74571\u201374585 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2988854","journal-title":"IEEE Access"},{"key":"649_CR18","doi-asserted-by":"crossref","unstructured":"Shobana, M., Poonkuzhali, S. A novel approach to detect IoT malware by system calls using Deep learning techniques. In: Proceedings of the 2020 International Conference on Innovative Trends in Information Technology (ICITIIT), Kottayam, India, 13\u201314 February 2020;1\u20135.","DOI":"10.1109\/ICITIIT49094.2020.9071531"},{"key":"649_CR19","doi-asserted-by":"publisher","unstructured":"Gupta, B.B., Quamara, M.: An overview of Internet of Things (IoT): Architectural aspects, challenges, and protocols. Concurr. Comput. Pract. Exp. 32 (2018). https:\/\/doi.org\/10.1002\/cpe.4946.","DOI":"10.1002\/cpe.4946"},{"key":"649_CR20","doi-asserted-by":"crossref","unstructured":"Tiwari, A. , Ritu, G. Adaptive ontology-based IoT resource provisioning in computing systems. Intl J Semant Web Inf Syst. 18, 1\u201318 (2022).","DOI":"10.4018\/IJSWIS.306260"},{"key":"649_CR21","doi-asserted-by":"publisher","unstructured":"Memos, V., Psannis, K., Ishibashi, Y., Kim, B.-G., Gupta, B. B.: An efficient algorithm for media-based surveillance system (EAMSuS) in IoT Smart City framework. Fut. Gen. Comput. Syst. 83 (2017). https:\/\/doi.org\/10.1016\/j.future.2017.04.039.","DOI":"10.1016\/j.future.2017.04.039"},{"key":"649_CR22","doi-asserted-by":"publisher","unstructured":"\u00d6zalp, A.N., Albayrak, Z., \u00c7akmak, M., \u00d6zdo\u011fan, E.: Layer-based examination of cyber-attacks in IoT. In: 2022 international congress on human-computer interaction, optimization and robotic applications (HORA), Ankara, Turkey, 2022, 1-10, https:\/\/doi.org\/10.1109\/HORA55278.2022.9800047","DOI":"10.1109\/HORA55278.2022.9800047"},{"key":"649_CR23","doi-asserted-by":"publisher","unstructured":"Altunay, H.C., Albayrak, Z., \u00d6zalp, A.N., \u00c7akmak, M.: Analysis of anomaly detection approaches performed through deep learning methods in SCADA systems. In: 2021 3rd international congress on human-computer interaction, optimization and robotic applications (HORA), Ankara, Turkey, 2021, 1-6, https:\/\/doi.org\/10.1109\/HORA52670.2021.9461273","DOI":"10.1109\/HORA52670.2021.9461273"},{"issue":"21","key":"649_CR24","doi-asserted-by":"publisher","first-page":"271","DOI":"10.12700\/APH.21.2.2024.2.15","volume":"2024","author":"E \u00d6zdo\u011fan","year":"2024","unstructured":"\u00d6zdo\u011fan, E., Erdem, A., \u00d6zalp, A.: Adaptive hybrid application protocol for IoT. Acta Polytechnica Hungarica 2024(21), 271\u2013290 (2024). https:\/\/doi.org\/10.12700\/APH.21.2.2024.2.15","journal-title":"Acta Polytechnica Hungarica"},{"key":"649_CR25","doi-asserted-by":"publisher","first-page":"102662","DOI":"10.1016\/j.jnca.2020.102662","volume":"163","author":"GDLT Parra","year":"2020","unstructured":"Parra, G.D.L.T., Rad, P., Choo, K.-K.R., Beebe, N.: Detecting Internet of Things attacks using distributed deep learning. J. Netw. Comput. Appl.Netw. Comput. Appl. 163, 102662 (2020). https:\/\/doi.org\/10.1016\/j.jnca.2020.102662","journal-title":"J. Netw. Comput. Appl.Netw. Comput. Appl."},{"key":"649_CR26","doi-asserted-by":"publisher","first-page":"101906","DOI":"10.1016\/j.cose.2020.101906","volume":"96","author":"W Haider","year":"2020","unstructured":"Haider, W., Moustafa, N., Keshk, M., Fernandez, A., Choo, K.-K.-R., Wahab, A.: FGMCHADS: fuzzy Gaussian mixture-based correntropy models for detecting zero-day attacks from linux systems. Comput. Secur.. Secur. 96, 101906 (2020). https:\/\/doi.org\/10.1016\/j.cose.2020.101906","journal-title":"Comput. Secur.. Secur."},{"key":"649_CR27","doi-asserted-by":"publisher","unstructured":"Ibitoye, O., Shafiq, O., Matrawy, A.: Analyzing adversarial attacks against deep learning for intrusion detection in IoT networks. https:\/\/doi.org\/10.1109\/GLOBECOM38437.2019.9014337","DOI":"10.1109\/GLOBECOM38437.2019.9014337"},{"key":"649_CR28","unstructured":"Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling, arXiv preprint arXiv, 2014,1412.3555."},{"key":"649_CR29","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1016\/j.neucom.2014.03.076","volume":"149","author":"B Wang","year":"2015","unstructured":"Wang, B., Huang, S., Qiu, J., et al.: Parallel online sequential extreme learning machine based on MapReduce. Neurocomputing 149, 224\u2013232 (2015). https:\/\/doi.org\/10.1016\/j.neucom.2014.03.076","journal-title":"Neurocomputing"},{"key":"649_CR30","doi-asserted-by":"publisher","unstructured":"Kalpana, P., Almusawi, M., Chanti, Y., Sunil Kumar, V., Varaprasad Rao, M.: A deep reinforcement learning-based task offloading framework for edge-cloud computing. In: 2024 International Conference on Integrated Circuits and Communication Systems (ICICACS), Raichur, India, 2024, 1\u20135, https:\/\/doi.org\/10.1109\/ICICACS60521.2024.10498232.","DOI":"10.1109\/ICICACS60521.2024.10498232"},{"key":"649_CR31","doi-asserted-by":"publisher","first-page":"101547","DOI":"10.1016\/j.imu.2024.101547","volume":"49","author":"SA Nabi","year":"2024","unstructured":"Nabi, S. A., Kalpana, P., Chandra, N. S., Smitha, L., Naresh, K., Ezugwu, A. E.,  Abualigah, L. Distributed private preserving learning based chaotic encryption framework for cognitive healthcare IoT systems. Inform. Med. Unlocked. 49, 101547 (2024). https:\/\/doi.org\/10.1016\/j.imu.2024.101547.","journal-title":"Inform. Med. Unlocked."},{"key":"649_CR32","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1016\/j.chemolab.2015.08.020","volume":"149","author":"F Marini","year":"2015","unstructured":"Marini, F., Walczak, B.: Particle swarm optimization (PSO). A tutorial. Chemom. Intell. Lab. Syst.. Intell. Lab. Syst. 149, 153\u2013165 (2015). https:\/\/doi.org\/10.1016\/j.chemolab.2015.08.020","journal-title":"Chemom. Intell. Lab. Syst.. Intell. Lab. Syst."},{"key":"649_CR33","doi-asserted-by":"publisher","unstructured":"Gurrola-Ramos, J., Hern\u00e0ndez-Aguirre, A., Dalmau-Cede\u00f1o, O.: COLSHADE for real-world single-objective constrained optimization problems. In: Proceedings of the 2020 IEEE Congress on Evolutionary Computation (CEC), Glasgow, UK, 2020, 19\u201324. https:\/\/doi.org\/10.1109\/CEC48606.2020.9185583","DOI":"10.1109\/CEC48606.2020.9185583"},{"key":"649_CR34","doi-asserted-by":"publisher","first-page":"100973","DOI":"10.1016\/j.swevo.2021.100973","volume":"67","author":"A LaTorre","year":"2021","unstructured":"LaTorre, A., Molina, D., Osaba, E., Poyatos, J., Del Ser, J., Herrera, F.: A prescription of methodological guidelines for comparing bio-inspired optimization algorithms. Swarm Evol. Comput.Evol. Comput. 67, 100973 (2021)","journal-title":"Swarm Evol. Comput.Evol. Comput."},{"key":"649_CR35","doi-asserted-by":"publisher","unstructured":"Jovanovic, L., Jovanovic, D., Bacanin, N., Jovancai Stakic, A., Antonijevic, M., Magd, H., Thirumalaisamy, R., Zivkovic, M.: Multistep crude oil price prediction based on lstm approach tuned by salp swarm algorithm with disputation operator. Sustainability. 191, 116158 (2022). https:\/\/doi.org\/10.1016\/j.energy.2021.120797","DOI":"10.1016\/j.energy.2021.120797"},{"key":"649_CR36","doi-asserted-by":"publisher","unstructured":"Abualigah, L., Abd Elaziz, M., Sumari, P., Geem, Z.W., Gandomi, A.H.: Reptile search algorithm (RSA): a nature-inspired meta-heuristic optimizer. https:\/\/doi.org\/10.1016\/j.eswa.2021.116158","DOI":"10.1016\/j.eswa.2021.116158"},{"key":"649_CR37","doi-asserted-by":"publisher","first-page":"119122","DOI":"10.1016\/j.ins.2023.119122","volume":"642","author":"N Bacanin","year":"2023","unstructured":"Bacanin, N., Jovanovic, L., Zivkovic, M., Kandasamy, V., Antonijevic, M., Deveci, M., Strumberger, I.: Multivariate energy forecasting via metaheuristic tuned long-short term memory and gated recurrent unit neural networks. Inf. Sci. 642, 119122 (2023). https:\/\/doi.org\/10.1016\/j.ins.2023.119122","journal-title":"Inf. Sci."}],"container-title":["International Journal of Computational Intelligence Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-024-00649-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44196-024-00649-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-024-00649-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T16:08:58Z","timestamp":1726502938000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44196-024-00649-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,16]]},"references-count":37,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["649"],"URL":"https:\/\/doi.org\/10.1007\/s44196-024-00649-8","relation":{},"ISSN":["1875-6883"],"issn-type":[{"value":"1875-6883","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,16]]},"assertion":[{"value":"22 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 August 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 September 2024","order":3,"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 that there are no conflicts of interest regarding the publication of this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}},{"value":"This article does not contain any studies involving human participants or animals conducted by the author.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}}],"article-number":"243"}}