{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T11:47:55Z","timestamp":1782992875259,"version":"3.54.5"},"reference-count":92,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T00:00:00Z","timestamp":1759968000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T00:00:00Z","timestamp":1759968000000},"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":["Int. J. Inf. Secur."],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1007\/s10207-025-01137-6","type":"journal-article","created":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T19:11:19Z","timestamp":1760037079000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Tackling smart city security: deep learning approach utilizing feature selection and two-level cooperative framework optimized by adapted metaheuristics algorithm"],"prefix":"10.1007","volume":"24","author":[{"given":"Katarina","family":"Kumpf","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miroslav","family":"Cajic","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vico","family":"Zeljkovic","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Milos","family":"Mravik","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miodrag","family":"Zivkovic","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joseph","family":"Mani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vladimir","family":"Simic","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nebojsa","family":"Bacanin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,9]]},"reference":[{"issue":"3","key":"1137_CR1","doi-asserted-by":"publisher","first-page":"76","DOI":"10.4018\/IJPADA.2018070106","volume":"5","author":"W Yu","year":"2018","unstructured":"Yu, W., Xu, C.: Developing smart cities in china: an empirical analysis. Int. J. Public Administration Digital Age (IJPADA) 5(3), 76\u201391 (2018)","journal-title":"Int. J. Public Administration Digital Age (IJPADA)"},{"issue":"2","key":"1137_CR2","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1080\/10630732.2011.601117","volume":"18","author":"A Caragliu","year":"2011","unstructured":"Caragliu, A., Del Bo, C., Nijkamp, P.: Smart cities in europe. J. Urban Technol. 18(2), 65\u201382 (2011)","journal-title":"J. Urban Technol."},{"key":"1137_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2017.03.034","volume-title":"Smart City and IoT","author":"T-H Kim","year":"2017","unstructured":"Kim, T.-H., Ramos, C., Mohammed, S.: Smart City and IoT. Elsevier, Amsterdam (2017)"},{"issue":"1","key":"1137_CR4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13174-014-0015-z","volume":"6","author":"E Al Nuaimi","year":"2015","unstructured":"Al Nuaimi, E., Al Neyadi, H., Mohamed, N., Al-Jaroodi, J.: Applications of big data to smart cities. J. Internet Services Appl. 6(1), 1\u201315 (2015)","journal-title":"J. Internet Services Appl."},{"key":"1137_CR5","unstructured":"OpenIoT \u2013 Open Source cloud solution for the Internet of Things. http:\/\/www.openiot.eu\/. Accessed: May 6, 2021 (2021)"},{"key":"1137_CR6","doi-asserted-by":"crossref","unstructured":"Miletiev, R., Iliev, I., Iontchev, E., Yordanov, R.: Continuous remote ammonia monitoring by air quality measurement and communication system. In: Future Access Enablers for Ubiquitous and Intelligent Infrastructures: 4th EAI International Conference, FABULOUS 2019, Sofia, Bulgaria, March 28-29, 2019, Proceedings 283, pp. 110\u2013117 (2019). Springer","DOI":"10.1007\/978-3-030-23976-3_11"},{"issue":"4","key":"1137_CR7","doi-asserted-by":"publisher","first-page":"2347","DOI":"10.1109\/COMST.2015.2444095","volume":"17","author":"A Al-Fuqaha","year":"2015","unstructured":"Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., Ayyash, M.: Internet of things: a survey on enabling technologies, protocols, and applications. IEEE Commun. Surv. Tutorials 17(4), 2347\u20132376 (2015)","journal-title":"IEEE Commun. Surv. Tutorials"},{"issue":"4","key":"1137_CR8","doi-asserted-by":"publisher","first-page":"2923","DOI":"10.1109\/COMST.2018.2844341","volume":"20","author":"M Mohammadi","year":"2018","unstructured":"Mohammadi, M., Al-Fuqaha, A., Sorour, S., Guizani, M.: Deep learning for iot big data and streaming analytics: a survey. IEEE Commun. Surv. Tutorials 20(4), 2923\u20132960 (2018)","journal-title":"IEEE Commun. Surv. Tutorials"},{"key":"1137_CR9","unstructured":"Ambre, O.V., Devalkar, O.D., Korgaonkar, G., Parab, S.R., Howale, R.A.: Smart street light system. Int. J. Adv. Res. Sci. Commun. Technol. (2023)"},{"issue":"2","key":"1137_CR10","doi-asserted-by":"publisher","first-page":"353","DOI":"10.3390\/en17020353","volume":"17","author":"JDJ Camacho","year":"2024","unstructured":"Camacho, J.D.J., Aguirre, B., Ponce, P., Anthony, B., Molina, A.: Leveraging artificial intelligence to bolster the energy sector in smart cities: a literature review. Energies 17(2), 353 (2024)","journal-title":"Energies"},{"issue":"4","key":"1137_CR11","doi-asserted-by":"publisher","first-page":"1959","DOI":"10.1007\/s10311-023-01604-3","volume":"21","author":"B Fang","year":"2023","unstructured":"Fang, B., Yu, J., Chen, Z., Osman, A.I., Farghali, M., Ihara, I., Hamza, E.H., Rooney, D.W., Yap, P.-S.: Artificial intelligence for waste management in smart cities: a review. Environ. Chem. Lett. 21(4), 1959\u20131989 (2023)","journal-title":"Environ. Chem. Lett."},{"key":"1137_CR12","doi-asserted-by":"crossref","unstructured":"Srivastava, S., Bisht, A., Narayan, N.: Safety and security in smart cities using artificial intelligence\u2014a review. In: 2017 7th International Conference on Cloud Computing, Data Science & Engineering-confluence, pp. 130\u2013133 (2017). IEEE","DOI":"10.1109\/CONFLUENCE.2017.7943136"},{"key":"1137_CR13","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.diin.2017.06.015","volume":"22","author":"ZA Baig","year":"2017","unstructured":"Baig, Z.A., Szewczyk, P., Valli, C., Rabadia, P., Hannay, P., Chernyshev, M., Johnstone, M., Kerai, P., Ibrahim, A., Sansurooah, K., et al.: Future challenges for smart cities: cyber-security and digital forensics. Digit. Investig. 22, 3\u201313 (2017)","journal-title":"Digit. Investig."},{"issue":"9","key":"1137_CR14","doi-asserted-by":"publisher","first-page":"285","DOI":"10.3390\/fi15090285","volume":"15","author":"F Almeida","year":"2023","unstructured":"Almeida, F.: Prospects of cybersecurity in smart cities. Future Internet 15(9), 285 (2023)","journal-title":"Future Internet"},{"key":"1137_CR15","doi-asserted-by":"crossref","unstructured":"Uwe, B.-R., Gerber, B.J.: Smart cities and the challenges of cross domain risk management: Considering interdependencies between ict-security and natural hazards disruptions. Econ. Culture 16(2), (2019)","DOI":"10.2478\/jec-2019-0026"},{"key":"1137_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110781","volume":"276","author":"Y Jia","year":"2023","unstructured":"Jia, Y., Gu, Z., Du, L., Long, Y., Wang, Y., Li, J., Zhang, Y.: Artificial intelligence enabled cyber security defense for smart cities: a novel attack detection framework based on the mdata model. Knowl.-Based Syst. 276, 110781 (2023)","journal-title":"Knowl.-Based Syst."},{"issue":"1","key":"1137_CR17","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1109\/4235.585893","volume":"1","author":"DH Wolpert","year":"1997","unstructured":"Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67\u201382 (1997). https:\/\/doi.org\/10.1109\/4235.585893","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"3","key":"1137_CR18","first-page":"543","volume":"22","author":"L Jovanovic","year":"2023","unstructured":"Jovanovic, L., Jovanovic, D., Antonijevic, M., Nikolic, B., Bacanin, N., Zivkovic, M., Strumberger, I.: Improving phishing website detection using a hybrid two-level framework for feature selection and xgboost tuning. J. Web Eng. 22(3), 543\u2013574 (2023)","journal-title":"J. Web Eng."},{"issue":"18","key":"1137_CR19","doi-asserted-by":"publisher","first-page":"2918","DOI":"10.3390\/math12182918","volume":"12","author":"A Petrovic","year":"2024","unstructured":"Petrovic, A., Jovanovic, L., Bacanin, N., Antonijevic, M., Savanovic, N., Zivkovic, M., Milovanovic, M., Gajic, V.: Exploring metaheuristic optimized machine learning for software defect detection on natural language and classical datasets. Mathematics 12(18), 2918 (2024)","journal-title":"Mathematics"},{"key":"1137_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2025.129695","volume":"630","author":"JP Villoth","year":"2025","unstructured":"Villoth, J.P., Zivkovic, M., Zivkovic, T., Abdel-salam, M., Hammad, M., Jovanovic, L., Simic, V., Bacanin, N.: Two-tier deep and machine learning approach optimized by adaptive multi-population firefly algorithm for software defects prediction. Neurocomputing 630, 129695 (2025)","journal-title":"Neurocomputing"},{"issue":"1","key":"1137_CR21","doi-asserted-by":"publisher","first-page":"3555","DOI":"10.1038\/s41598-025-88135-9","volume":"15","author":"M Antonijevic","year":"2025","unstructured":"Antonijevic, M., Zivkovic, M., Djuric Jovicic, M., Nikolic, B., Perisic, J., Milovanovic, M., Jovanovic, L., Abdel-Salam, M., Bacanin, N.: Intrusion detection in metaverse environment internet of things systems by metaheuristics tuned two level framework. Sci. Rep. 15(1), 3555 (2025)","journal-title":"Sci. Rep."},{"issue":"11","key":"1137_CR22","doi-asserted-by":"publisher","first-page":"1097","DOI":"10.1016\/S0305-0548(97)00031-2","volume":"24","author":"N Mladenovi\u0107","year":"1997","unstructured":"Mladenovi\u0107, N., Hansen, P.: Variable neighborhood search. Comput. Oper. Res. 24(11), 1097\u20131100 (1997). https:\/\/doi.org\/10.1016\/S0305-0548(97)00031-2","journal-title":"Comput. Oper. Res."},{"key":"1137_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2020.106906","volume":"89","author":"S Khan","year":"2021","unstructured":"Khan, S., Nazir, S., Garc\u00eda-Magari\u00f1o, I., Hussain, A.: Deep learning-based urban big data fusion in smart cities: towards traffic monitoring and flow-preserving fusion. Comput. Electr. Eng. 89, 106906 (2021)","journal-title":"Comput. Electr. Eng."},{"key":"1137_CR24","doi-asserted-by":"crossref","unstructured":"Meng, C., Yi, X., Su, L., Gao, J., Zheng, Y.: City-wide traffic volume inference with loop detector data and taxi trajectories. In: Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 1\u201310 (2017)","DOI":"10.1145\/3139958.3139984"},{"key":"1137_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.scs.2020.102500","volume":"64","author":"S Majumdar","year":"2021","unstructured":"Majumdar, S., Subhani, M.M., Roullier, B., Anjum, A., Zhu, R.: Congestion prediction for smart sustainable cities using iot and machine learning approaches. Sustain. Cities Soc. 64, 102500 (2021)","journal-title":"Sustain. Cities Soc."},{"issue":"1","key":"1137_CR26","doi-asserted-by":"publisher","first-page":"1607","DOI":"10.1007\/s40747-023-01175-4","volume":"10","author":"A Ullah","year":"2024","unstructured":"Ullah, A., Anwar, S.M., Li, J., Nadeem, L., Mahmood, T., Rehman, A., Saba, T.: Smart cities: the role of internet of things and machine learning in realizing a data-centric smart environment. Complex Intell. Syst. 10(1), 1607\u20131637 (2024)","journal-title":"Complex Intell. Syst."},{"key":"1137_CR27","doi-asserted-by":"publisher","first-page":"185059","DOI":"10.1109\/ACCESS.2020.3029943","volume":"8","author":"W Ahmed","year":"2020","unstructured":"Ahmed, W., Ansari, H., Khan, B., Ullah, Z., Ali, S.M., Mehmood, C.A.A., Qureshi, M.B., Hussain, I., Jawad, M., Khan, M.U.S., et al.: Machine learning based energy management model for smart grid and renewable energy districts. IEEE Access 8, 185059\u2013185078 (2020)","journal-title":"IEEE Access"},{"key":"1137_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.scs.2020.102655","volume":"66","author":"D Chen","year":"2021","unstructured":"Chen, D., Wawrzynski, P., Lv, Z.: Cyber security in smart cities: a review of deep learning-based applications and case studies. Sustain. Cities Soc. 66, 102655 (2021)","journal-title":"Sustain. Cities Soc."},{"key":"1137_CR29","doi-asserted-by":"crossref","unstructured":"Rashid, M.M., Kamruzzaman, J., Imam, T., Kaisar, S., Alam, M.J.: Cyber attacks detection from smart city applications using artificial neural network. In: 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), pp. 1\u20136 (2020). IEEE","DOI":"10.1109\/CSDE50874.2020.9411606"},{"key":"1137_CR30","doi-asserted-by":"publisher","first-page":"23733","DOI":"10.1109\/ACCESS.2024.3363469","volume":"12","author":"MA Ferrag","year":"2024","unstructured":"Ferrag, M.A., Ndhlovu, M., Tihanyi, N., Cordeiro, L.C., Debbah, M., Lestable, T., Thandi, N.S.: Revolutionizing cyber threat detection with large language models: a privacy-preserving bert-based lightweight model for iot\/iiot devices. IEEE Access 12, 23733\u201323750 (2024)","journal-title":"IEEE Access"},{"issue":"3","key":"1137_CR31","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1049\/smc2.12084","volume":"6","author":"Y Ahmed","year":"2024","unstructured":"Ahmed, Y., Beyioku, K., Yousefi, M.: Securing smart cities through machine learning: a honeypot-driven approach to attack detection in internet of things ecosystems. IET Smart Cities 6(3), 180\u2013198 (2024)","journal-title":"IET Smart Cities"},{"key":"1137_CR32","doi-asserted-by":"crossref","unstructured":"Pang, Y., Li, C.: Enhancing cybersecurity in iot networks: A deep learning approach to anomaly detection. In: 2024 IEEE International Conference on e-Business Engineering (ICEBE), pp. 139\u2013144 (2024). IEEE","DOI":"10.1109\/ICEBE62490.2024.00030"},{"issue":"24","key":"1137_CR33","doi-asserted-by":"publisher","first-page":"9347","DOI":"10.3390\/ijerph17249347","volume":"17","author":"MM Rashid","year":"2020","unstructured":"Rashid, M.M., Kamruzzaman, J., Hassan, M.M., Imam, T., Gordon, S.: Cyberattacks detection in iot-based smart city applications using machine learning techniques. Int. J. Environ. Res. Public Health 17(24), 9347 (2020)","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"1137_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.teler.2022.100030","volume":"8","author":"S Jain","year":"2022","unstructured":"Jain, S., Pawar, P.M., Muthalagu, R.: Hybrid intelligent intrusion detection system for internet of things. Telematics Inf. Rep. 8, 100030 (2022)","journal-title":"Telematics Inf. Rep."},{"issue":"7","key":"1137_CR35","doi-asserted-by":"publisher","first-page":"9975","DOI":"10.1007\/s10586-024-04483-7","volume":"27","author":"N Soltani","year":"2024","unstructured":"Soltani, N., Rahmani, A.M., Bohlouli, M., Hosseinzadeh, M.: Robust intrusion detection for network communication on the internet of things: a hybrid machine learning approach. Clust. Comput. 27(7), 9975\u20139991 (2024)","journal-title":"Clust. Comput."},{"key":"1137_CR36","doi-asserted-by":"crossref","unstructured":"Alamro, H., Marzouk, R., Alruwais, N., Negm, N., Aljameel, S.S., Khalid, M., Hamza, M.A., Alsaid, M.I.: Modelling of blockchain assisted intrusion detection on iot healthcare system using ant lion optimizer with hybrid deep learning. IEEE Access (2023)","DOI":"10.1109\/ACCESS.2023.3299589"},{"key":"1137_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.123808","volume":"249","author":"H Nandanwar","year":"2024","unstructured":"Nandanwar, H., Katarya, R.: Deep learning enabled intrusion detection system for industrial iot environment. Expert Syst. Appl. 249, 123808 (2024)","journal-title":"Expert Syst. Appl."},{"issue":"2","key":"1137_CR38","doi-asserted-by":"publisher","first-page":"1251","DOI":"10.1007\/s10207-023-00787-8","volume":"23","author":"H Nandanwar","year":"2024","unstructured":"Nandanwar, H., Katarya, R.: Tl-bilstm iot: transfer learning model for prediction of intrusion detection system in iot environment. Int. J. Inf. Secur. 23(2), 1251\u20131277 (2024)","journal-title":"Int. J. Inf. Secur."},{"key":"1137_CR39","doi-asserted-by":"crossref","unstructured":"Nandanwar, H., Katarya, R.: Optimized intrusion detection and secure data management in iot networks using gao-xgboost and ecc-integrated blockchain framework. Knowledge Inf. Syst. 1\u201356 (2025)","DOI":"10.1007\/s10115-025-02513-3"},{"issue":"10","key":"1137_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2023.101820","volume":"35","author":"A Nazir","year":"2023","unstructured":"Nazir, A., He, J., Zhu, N., Wajahat, A., Ma, X., Ullah, F., Qureshi, S., Pathan, M.S.: Advancing iot security: a systematic review of machine learning approaches for the detection of iot botnets. J. King Saud University-Comput. Inf. Sci. 35(10), 101820 (2023)","journal-title":"J. King Saud University-Comput. Inf. Sci."},{"issue":"6","key":"1137_CR41","doi-asserted-by":"publisher","first-page":"775","DOI":"10.1007\/s11227-025-07255-1","volume":"81","author":"A Nazir","year":"2025","unstructured":"Nazir, A., He, J., Zhu, N., Wajahat, A., Ullah, F., Qureshi, S., Pathan, M.S.: Empirical evaluation of ensemble learning and hybrid cnn-lstm for iot threat detection on heterogeneous datasets. J. Supercomput. 81(6), 775 (2025)","journal-title":"J. Supercomput."},{"issue":"2","key":"1137_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2024.101939","volume":"36","author":"A Nazir","year":"2024","unstructured":"Nazir, A., He, J., Zhu, N., Wajahat, A., Ullah, F., Qureshi, S., Ma, X., Pathan, M.S.: Collaborative threat intelligence: enhancing iot security through blockchain and machine learning integration. J. King Saud University-Comput. Inf. Sci. 36(2), 101939 (2024)","journal-title":"J. King Saud University-Comput. Inf. Sci."},{"issue":"6","key":"1137_CR43","doi-asserted-by":"publisher","first-page":"8367","DOI":"10.1007\/s10586-024-04436-0","volume":"27","author":"A Nazir","year":"2024","unstructured":"Nazir, A., He, J., Zhu, N., Anwar, M.S., Pathan, M.S.: Enhancing iot security: a collaborative framework integrating federated learning, dense neural networks, and blockchain. Clust. Comput. 27(6), 8367\u20138392 (2024)","journal-title":"Clust. Comput."},{"key":"1137_CR44","doi-asserted-by":"crossref","unstructured":"Goran, R., Jovanovic, L., Bacanin, N., Stankovic, M., Simic, V., Antonijevic, M., Zivkovic, M.: Identifying and understanding student dropouts using metaheuristic optimized classifiers and explainable artificial intelligence techniques. IEEE Access (2024)","DOI":"10.1109\/ACCESS.2024.3446653"},{"key":"1137_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2024.104048","volume":"233","author":"A Jovanovic","year":"2025","unstructured":"Jovanovic, A., Jovanovic, L., Zivkovic, M., Bacanin, N., Simic, V., Pamucar, D., Antonijevic, M.: Particle swarm optimization tuned multi-headed long short-term memory networks approach for fuel prices forecasting. J. Netw. Comput. Appl. 233, 104048 (2025)","journal-title":"J. Netw. Comput. Appl."},{"key":"1137_CR46","doi-asserted-by":"crossref","unstructured":"Bacanin, N., Stoean, C., Markovic, D., Zivkovic, M., Rashid, T.A., Chhabra, A., Sarac, M.: Improving performance of extreme learning machine for classification challenges by modified firefly algorithm and validation on medical benchmark datasets. Multimed. Tools Appl. 1\u201341 (2024)","DOI":"10.1007\/s11042-024-18295-9"},{"issue":"4","key":"1137_CR47","doi-asserted-by":"publisher","first-page":"394","DOI":"10.3390\/toxics11040394","volume":"11","author":"G Jovanovic","year":"2023","unstructured":"Jovanovic, G., Perisic, M., Bacanin, N., Zivkovic, M., Stanisic, S., Strumberger, I., Alimpic, F., Stojic, A.: Potential of coupling metaheuristics-optimized-xgboost and shap in revealing pahs environmental fate. Toxics 11(4), 394 (2023)","journal-title":"Toxics"},{"issue":"10","key":"1137_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2024.102261","volume":"36","author":"S Purkovic","year":"2024","unstructured":"Purkovic, S., Jovanovic, L., Zivkovic, M., Antonijevic, M., Dolicanin, E., Tuba, E., Tuba, M., Bacanin, N., Spalevic, P.: Audio analysis with convolutional neural networks and boosting algorithms tuned by metaheuristics for respiratory condition classification. J. King Saud University-Comput. Inf. Sci. 36(10), 102261 (2024)","journal-title":"J. King Saud University-Comput. Inf. Sci."},{"issue":"22","key":"1137_CR49","doi-asserted-by":"publisher","first-page":"3798","DOI":"10.3390\/electronics11223798","volume":"11","author":"M Zivkovic","year":"2022","unstructured":"Zivkovic, M., Bacanin, N., Antonijevic, M., Nikolic, B., Kvascev, G., Marjanovic, M., Savanovic, N.: Hybrid cnn and xgboost model tuned by modified arithmetic optimization algorithm for covid-19 early diagnostics from x-ray images. Electronics 11(22), 3798 (2022)","journal-title":"Electronics"},{"issue":"13","key":"1137_CR50","doi-asserted-by":"publisher","first-page":"5941","DOI":"10.3390\/s23135941","volume":"23","author":"ECP Neto","year":"2023","unstructured":"Neto, E.C.P., Dadkhah, S., Ferreira, R., Zohourian, A., Lu, R., Ghorbani, A.A.: Ciciot 2023: a real-time dataset and benchmark for large-scale attacks in iot environment. Sensors 23(13), 5941 (2023)","journal-title":"Sensors"},{"key":"1137_CR51","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1016\/j.patcog.2017.10.013","volume":"77","author":"J Gu","year":"2018","unstructured":"Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern Recogn. 77, 354\u2013377 (2018)","journal-title":"Pattern Recogn."},{"issue":"1","key":"1137_CR52","first-page":"6973103","volume":"2018","author":"S Albawi","year":"2018","unstructured":"Albawi, S., Bayat, O., Al-Azawi, S., Ucan, O.N.: Social touch gesture recognition using convolutional neural network. Comput. Intell. Neurosci. 2018(1), 6973103 (2018)","journal-title":"Comput. Intell. Neurosci."},{"key":"1137_CR53","unstructured":"Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML-10), pp. 807\u2013814 (2010)"},{"issue":"6","key":"1137_CR54","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1109\/MSP.2012.2205597","volume":"29","author":"G Hinton","year":"2012","unstructured":"Hinton, G., Deng, L., Yu, D., Dahl, G.E., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T.N., Kingsbury, B.: Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process. Mag. 29(6), 82\u201397 (2012). https:\/\/doi.org\/10.1109\/MSP.2012.2205597","journal-title":"IEEE Signal Process. Mag."},{"issue":"19","key":"1137_CR55","doi-asserted-by":"publisher","first-page":"6654","DOI":"10.3390\/s21196654","volume":"21","author":"J Basha","year":"2021","unstructured":"Basha, J., Bacanin, N., Vukobrat, N., Zivkovic, M., Venkatachalam, K., Hub\u00e1lovsk\u1ef3, S., Trojovsk\u1ef3, P.: Chaotic harris hawks optimization with quasi-reflection-based learning: an application to enhance cnn design. Sensors 21(19), 6654 (2021)","journal-title":"Sensors"},{"issue":"1","key":"1137_CR56","doi-asserted-by":"publisher","first-page":"6302","DOI":"10.1038\/s41598-022-09744-2","volume":"12","author":"N Bacanin","year":"2022","unstructured":"Bacanin, N., Zivkovic, M., Al-Turjman, F., Venkatachalam, K., Trojovsk\u1ef3, P., Strumberger, I., Bezdan, T.: Hybridized sine cosine algorithm with convolutional neural networks dropout regularization application. Sci. Rep. 12(1), 6302 (2022)","journal-title":"Sci. Rep."},{"issue":"6","key":"1137_CR57","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Commun. ACM 60(6), 84\u201390 (2017)","journal-title":"Commun. ACM"},{"key":"1137_CR58","doi-asserted-by":"crossref","unstructured":"Ranjan, R., Sankaranarayanan, S., Castillo, C.D., Chellappa, R.: An all-in-one convolutional neural network for face analysis. In: 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), pp. 17\u201324 (2017). IEEE","DOI":"10.1109\/FG.2017.137"},{"issue":"10","key":"1137_CR59","doi-asserted-by":"publisher","first-page":"110","DOI":"10.3390\/jimaging6100110","volume":"6","author":"F Lombardi","year":"2020","unstructured":"Lombardi, F., Marinai, S.: Deep learning for historical document analysis and recognition\u2013a survey. J. Imaging 6(10), 110 (2020)","journal-title":"J. Imaging"},{"issue":"11","key":"1137_CR60","doi-asserted-by":"publisher","first-page":"713","DOI":"10.21037\/atm.2020.02.44","volume":"8","author":"L Cai","year":"2020","unstructured":"Cai, L., Gao, J., Zhao, D.: A review of the application of deep learning in medical image classification and segmentation. Ann. Trans. Med. 8(11), 713 (2020)","journal-title":"Ann. Trans. Med."},{"issue":"23","key":"1137_CR61","doi-asserted-by":"publisher","first-page":"12687","DOI":"10.3390\/app132312687","volume":"13","author":"M Salb","year":"2023","unstructured":"Salb, M., Jovanovic, L., Bacanin, N., Antonijevic, M., Zivkovic, M., Budimirovic, N., Abualigah, L.: Enhancing internet of things network security using hybrid cnn and xgboost model tuned via modified reptile search algorithm. Appl. Sci. 13(23), 12687 (2023)","journal-title":"Appl. Sci."},{"key":"1137_CR62","doi-asserted-by":"crossref","unstructured":"Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785\u2013794 (2016)","DOI":"10.1145\/2939672.2939785"},{"key":"1137_CR63","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijpvp.2022.104655","volume":"197","author":"W Liu","year":"2022","unstructured":"Liu, W., Chen, Z., Hu, Y.: Xgboost algorithm-based prediction of safety assessment for pipelines. Int. J. Press. Vessels Pip. 197, 104655 (2022)","journal-title":"Int. J. Press. Vessels Pip."},{"issue":"2","key":"1137_CR64","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1214\/aos\/1016218223","volume":"28","author":"J Friedman","year":"2000","unstructured":"Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). Ann. Stat. 28(2), 337\u2013407 (2000)","journal-title":"Ann. Stat."},{"key":"1137_CR65","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2025.113086","volume":"175","author":"A Tasic","year":"2025","unstructured":"Tasic, A., Jovanovic, L., Bacanin, N., Zivkovic, M., Simic, V., Popovic, M., Antonijevic, M.: Towards sustainable societies: convolutional neural networks optimized by modified crayfish optimization algorithm aided by adaboost and xgboost for waste classification tasks. Appl. Soft Comput. 175, 113086 (2025)","journal-title":"Appl. Soft Comput."},{"issue":"3","key":"1137_CR66","first-page":"349","volume":"2","author":"T Hastie","year":"2009","unstructured":"Hastie, T., Rosset, S., Zhu, J., Zou, H.: Multi-class adaboost. statistics and its. Interface 2(3), 349\u2013360 (2009)","journal-title":"Interface"},{"issue":"6","key":"1137_CR67","first-page":"745","volume":"39","author":"C Ying","year":"2013","unstructured":"Ying, C., Qi-Guang, M., Jia-Chen, L., Lin, G.: Advance and prospects of adaboost algorithm. Acta Autom. Sin. 39(6), 745\u2013758 (2013)","journal-title":"Acta Autom. Sin."},{"key":"1137_CR68","doi-asserted-by":"crossref","unstructured":"Schapire, R.E.: Explaining adaboost. In: Empirical Inference: Festschrift in Honor of Vladimir N. Vapnik, pp. 37\u201352. Springer (2013)","DOI":"10.1007\/978-3-642-41136-6_5"},{"issue":"14","key":"1137_CR69","doi-asserted-by":"publisher","first-page":"17867","DOI":"10.1007\/s10639-024-12529-x","volume":"29","author":"N Mansouri","year":"2024","unstructured":"Mansouri, N., Abed, M., Soui, M.: Sbs feature selection and adaboost classifier for specialization\/major recommendation for undergraduate students. Educ. Inf. Technol. 29(14), 17867\u201317887 (2024)","journal-title":"Educ. Inf. Technol."},{"issue":"7","key":"1137_CR70","doi-asserted-by":"publisher","first-page":"3105","DOI":"10.3390\/app14073105","volume":"14","author":"SS Hussain","year":"2024","unstructured":"Hussain, S.S., Zaidi, S.S.H.: Adaboost ensemble approach with weak classifiers for gear fault diagnosis and prognosis in dc motors. Appl. Sci. 14(7), 3105 (2024)","journal-title":"Appl. Sci."},{"issue":"462","key":"1137_CR71","doi-asserted-by":"publisher","first-page":"324","DOI":"10.1198\/016214503000125","volume":"98","author":"P B\u00fchlmann","year":"2003","unstructured":"B\u00fchlmann, P., Yu, B.: Boosting with the l 2 loss: regression and classification. J. Am. Stat. Assoc. 98(462), 324\u2013339 (2003)","journal-title":"J. Am. Stat. Assoc."},{"key":"1137_CR72","unstructured":"Mason, L., Baxter, J., Bartlett, P., Frean, M.: Boosting algorithms as gradient descent. Adv. Neural Inf. Process. Syst. 12, (1999)"},{"issue":"3","key":"1137_CR73","first-page":"349","volume":"2","author":"J Zhu","year":"2009","unstructured":"Zhu, J., Zou, H., Rosset, S., Hastie, T., et al.: Multi-class adaboost. statistics and its. Interface 2(3), 349\u2013360 (2009)","journal-title":"Interface"},{"key":"1137_CR74","doi-asserted-by":"crossref","unstructured":"Kumpf, K., Protic, M., Jovanovic, L., Cajic, M., Zivkovic, M., Bacanin, N.: Insider threat detection using bidirectional encoder representations from transformers and optimized adaboost classifier. In: 2024 International Conference on Circuit, Systems and Communication (ICCSC), pp. 1\u20136 (2024). IEEE","DOI":"10.1109\/ICCSC62074.2024.10616526"},{"key":"1137_CR75","doi-asserted-by":"crossref","unstructured":"Villoth, S.J., Villoth, J.P., Zivkovic, T., Zivkovic, M., Jovanovic, L., Bacanin, N., Mani, J.: Adaboost optimized by sinh cosh algorithm for prediction of software defects. In: International Conference on Intelligent Systems and Pattern Recognition, pp. 53\u201368 (2024). Springer","DOI":"10.1007\/978-3-031-82153-0_5"},{"issue":"6","key":"1137_CR76","doi-asserted-by":"publisher","first-page":"7987","DOI":"10.1007\/s40747-024-01592-z","volume":"10","author":"B Radomirovic","year":"2024","unstructured":"Radomirovic, B., Bacanin, N., Jovanovic, L., Simic, V., Njegus, A., Pamucar, D., K\u00f6ppen, M., Zivkovic, M.: Optimizing long-short term memory neural networks for electroencephalogram anomaly detection using variable neighborhood search with dynamic strategy change. Complex Intell. Syst. 10(6), 7987\u20138009 (2024)","journal-title":"Complex Intell. Syst."},{"key":"1137_CR77","doi-asserted-by":"crossref","unstructured":"Li, X., Chen, N., Ma, H., Nie, F., Wang, X.: A parallel genetic algorithm with variable neighborhood search for the vehicle routing problem in forest fire-fighting. IEEE Transactions on Intelligent Transportation Systems (2024)","DOI":"10.1109\/TITS.2024.3395930"},{"key":"1137_CR78","doi-asserted-by":"publisher","DOI":"10.1016\/j.cor.2023.106480","volume":"163","author":"I Krimi","year":"2024","unstructured":"Krimi, I., Benmansour, R.: Self-adaptive general variable neighborhood search algorithm for parallel machine scheduling with unrelated servers. Comput. Oper. Res. 163, 106480 (2024)","journal-title":"Comput. Oper. Res."},{"issue":"24","key":"1137_CR79","doi-asserted-by":"publisher","first-page":"13489","DOI":"10.1007\/s00500-019-03887-7","volume":"23","author":"MA Al-Betar","year":"2019","unstructured":"Al-Betar, M.A., Aljarah, I., Awadallah, M.A., Faris, H., Mirjalili, S.: Adaptive $$\\beta $$-hill climbing for optimization. Soft. Comput. 23(24), 13489\u201313512 (2019)","journal-title":"Soft. Comput."},{"key":"1137_CR80","doi-asserted-by":"crossref","unstructured":"Mirjalili, S., Mirjalili, S.: Genetic algorithm. Evolutionary algorithms and neural networks: Theory and applications, 43\u201355 (2019)","DOI":"10.1007\/978-3-319-93025-1_4"},{"issue":"2","key":"1137_CR81","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5121\/ijdkp.2015.5201","volume":"5","author":"M Hossin","year":"2015","unstructured":"Hossin, M., Sulaiman, M.N.: A review on evaluation metrics for data classification evaluations. Int. J. Data Mining Knowledge Management Process 5(2), 1 (2015)","journal-title":"Int. J. Data Mining Knowledge Management Process"},{"issue":"2","key":"1137_CR82","doi-asserted-by":"publisher","first-page":"442","DOI":"10.1016\/0005-2795(75)90109-9","volume":"405","author":"BW Matthews","year":"1975","unstructured":"Matthews, B.W.: Comparison of the predicted and observed secondary structure of t4 phage lysozyme. Biochimica et Biophysica Acta (BBA)-Protein Structure 405(2), 442\u2013451 (1975)","journal-title":"Biochimica et Biophysica Acta (BBA)-Protein Structure"},{"key":"1137_CR83","doi-asserted-by":"publisher","unstructured":"Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN\u201995 - International Conference on Neural Networks, 4, 1942\u201319484 (1995). https:\/\/doi.org\/10.1109\/ICNN.1995.488968","DOI":"10.1109\/ICNN.1995.488968"},{"issue":"1","key":"1137_CR84","first-page":"36","volume":"1","author":"X-S Yang","year":"2013","unstructured":"Yang, X.-S., He, X.: Firefly algorithm: recent advances and applications. Int. J. Swarm Intell. 1(1), 36\u201350 (2013)","journal-title":"Int. J. Swarm Intell."},{"key":"1137_CR85","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/j.advengsoft.2017.07.002","volume":"114","author":"S Mirjalili","year":"2017","unstructured":"Mirjalili, S., Gandomi, A.H., Mirjalili, S.Z., Saremi, S., Faris, H., Mirjalili, S.M.: Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv. Eng. Softw. 114, 163\u2013191 (2017). https:\/\/doi.org\/10.1016\/j.advengsoft.2017.07.002","journal-title":"Adv. Eng. Softw."},{"key":"1137_CR86","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.111081","volume":"282","author":"J Bai","year":"2023","unstructured":"Bai, J., Li, Y., Zheng, M., Khatir, S., Benaissa, B., Abualigah, L., Abdel Wahab, M.: A sinh cosh optimizer. Knowl.-Based Syst. 282, 111081 (2023). https:\/\/doi.org\/10.1016\/j.knosys.2023.111081","journal-title":"Knowl.-Based Syst."},{"key":"1137_CR87","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113338","volume":"149","author":"M Khishe","year":"2020","unstructured":"Khishe, M., Mosavi, M.R.: Chimp optimization algorithm. Expert Syst. Appl. 149, 113338 (2020). https:\/\/doi.org\/10.1016\/j.eswa.2020.113338","journal-title":"Expert Syst. Appl."},{"key":"1137_CR88","doi-asserted-by":"publisher","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. 67, 100973 (2021). https:\/\/doi.org\/10.1016\/j.swevo.2021.100973","journal-title":"Swarm Evol. Comput."},{"issue":"4","key":"1137_CR89","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1093\/sysbio\/34.4.449","volume":"34","author":"BB Schultz","year":"1985","unstructured":"Schultz, B.B.: Levene\u2019s test for relative variation. Syst. Biol. 34(4), 449\u2013456 (1985). https:\/\/doi.org\/10.1093\/sysbio\/34.4.449","journal-title":"Syst. Biol."},{"issue":"337","key":"1137_CR90","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1080\/01621459.1972.10481232","volume":"67","author":"SS Shapiro","year":"1972","unstructured":"Shapiro, S.S., Francia, R.S.: An approximate analysis of variance test for normality. J. Am. Stat. Assoc. 67(337), 215\u2013216 (1972). https:\/\/doi.org\/10.1080\/01621459.1972.10481232","journal-title":"J. Am. Stat. Assoc."},{"key":"1137_CR91","doi-asserted-by":"publisher","unstructured":"Woolson, R.F.: Wilcoxon signed-rank test (2005). https:\/\/doi.org\/10.1002\/0470011815.b2a15177","DOI":"10.1002\/0470011815.b2a15177"},{"key":"1137_CR92","unstructured":"Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS\u201917, pp. 4768\u20134777. Curran Associates Inc., Red Hook, NY, USA (2017)"}],"container-title":["International Journal of Information Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10207-025-01137-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10207-025-01137-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10207-025-01137-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T08:15:27Z","timestamp":1764576927000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10207-025-01137-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,9]]},"references-count":92,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["1137"],"URL":"https:\/\/doi.org\/10.1007\/s10207-025-01137-6","relation":{},"ISSN":["1615-5262","1615-5270"],"issn-type":[{"value":"1615-5262","type":"print"},{"value":"1615-5270","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,9]]},"assertion":[{"value":"7 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 October 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 October 2025","order":5,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Update","order":6,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The original online version of this article was revised: the sixth affiliation has been corrected.","order":7,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no potential conflict of interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of Interest"}},{"value":"Relevant code samples are available on Zenodo, DOI: 10.5281\/zenodo.17204333,\n                      \n                      .","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code availability"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"221"}}