{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T11:07:28Z","timestamp":1763636848590,"version":"3.45.0"},"reference-count":41,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T00:00:00Z","timestamp":1763596800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"multidisciplinary project \u201cNew methods based on machine learning for the detection of cyber-attacks in IoT environments,\u201d","award":["098-2025-R-UPNW"],"award-info":[{"award-number":["098-2025-R-UPNW"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>The growing sophistication of cyberattacks in Internet of Things (IoT) environments demands proactive and efficient solutions. We present an automated hyperparameter optimization (HPO) method for detecting cyberattacks in IoT that explicitly addresses class imbalance. The approach combines a Random Forest surrogate, a UCB acquisition function with controlled exploration, and an objective function that maximizes weighted F1 and MCC; it also integrates stratified validation and a compact selection of descriptors by metaheuristic consensus. Five models (RandomForest, AdaBoost, DecisionTree, XGBoost, and MLP) were evaluated on CICIoT2023 and CIC-DDoS2019. The results show systematic improvements over default configurations and competitiveness compared to Hyperopt and GridSearch. For RandomForest, marked increases were observed in CIC-DDoS2019 (F1-Score from 0.9469 to 0.9995; MCC from 0.9284 to 0.9986) and consistent improvements in CICIoT2023 (F1-Score from 0.9947 to 0.9954; MCC from 0.9885 to 0.9896), while maintaining low inference times. These results demonstrate that the proposed HPO offers a solid balance between performance, computational cost, and traceability, and constitutes a reproducible alternative for strengthening cybersecurity mechanisms in IoT environments with limited resources.<\/jats:p>","DOI":"10.3390\/informatics12040126","type":"journal-article","created":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T10:30:59Z","timestamp":1763634659000},"page":"126","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automated Hyperparameter Optimization for Cyberattack Detection Based on Machine Learning in IoT Systems"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7445-7132","authenticated-orcid":false,"given":"Fray L.","family":"Becerra-Suarez","sequence":"first","affiliation":[{"name":"Grupo de Investigaci\u00f3n en Inteligencia Artificial (UMA-AI), Facultad de Ingenier\u00eda y Negocios, Universidad Privada Norbert Wiener, Lima 15046, Peru"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5569-8739","authenticated-orcid":false,"given":"Lloy","family":"Pinedo","sequence":"additional","affiliation":[{"name":"Grupo de Investigaci\u00f3n Transformaci\u00f3n Digital Empresarial, Facultad de Ingenier\u00eda y Negocios, Universidad Privada Norbert Wiener, Lima 15046, Peru"}]},{"given":"Madeleine J.","family":"Gavil\u00e1n-Colca","sequence":"additional","affiliation":[{"name":"Grupo de Investigaci\u00f3n en Inteligencia Artificial (UMA-AI), Facultad de Ingenier\u00eda y Negocios, Universidad Privada Norbert Wiener, Lima 15046, Peru"}]},{"given":"M\u00f3nica","family":"D\u00edaz","sequence":"additional","affiliation":[{"name":"Grupo de Investigaci\u00f3n en Inteligencia Artificial (UMA-AI), Facultad de Ingenier\u00eda y Negocios, Universidad Privada Norbert Wiener, Lima 15046, Peru"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9972-8621","authenticated-orcid":false,"given":"Manuel G.","family":"Forero","sequence":"additional","affiliation":[{"name":"Semillero L\u00fan, Grupo D+Tec, Facultad de Ingenier\u00eda, Universidad de Ibagu\u00e9, Ibagu\u00e9 730001, Colombia"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e928","DOI":"10.51252\/rcsi.v5i2.928","article-title":"Applications of Artificial Intelligence in Hospital Quality Management: A Review of Digital Strategies in Healthcare Settings","volume":"5","year":"2025","journal-title":"Rev. Cient. Sist. Inform."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Mahoto, N.A., Shaikh, A., Sulaiman, A., Reshan, M.S.A., Rajab, A., and Rajab, K. (2023). A Machine Learning Based Data Modeling for Medical Diagnosis. Biomed. Signal Process. Control, 81.","DOI":"10.1016\/j.bspc.2022.104481"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e966","DOI":"10.51252\/rcsi.v5i2.996","article-title":"Optimized Deep Neural Network for High-Precision Psoriasis Classification from Dermoscopic Images","volume":"5","author":"Bolia","year":"2025","journal-title":"Rev. Cient. Sist. Inform."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"172","DOI":"10.56294\/ai2025172","article-title":"Comparison of kernel functions in the prediction of cardiovascular disease in Artificial Neural Networks (ANN) and Support Vector Machines (SVM)","volume":"4","author":"Rodriguez","year":"2025","journal-title":"EthAIca"},{"key":"ref_5","first-page":"e671","article-title":"Sistemas Inteligentes y su Aplicaci\u00f3n en la Evaluaci\u00f3n del Desempe\u00f1o Acad\u00e9mico Universitario: Una Revisi\u00f3n de la Literatura en el Contexto Sudamericano","volume":"4","year":"2024","journal-title":"Rev. Cient. Sist. Inform."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Garikapati, D., and Shetiya, S.S. (2024). Autonomous Vehicles: Evolution of Artificial Intelligence and the Current Industry Landscape. Big Data Cogn. Comput., 8.","DOI":"10.3390\/bdcc8040042"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"25933","DOI":"10.1109\/ACCESS.2024.3366990","article-title":"Advancing Autonomous Vehicle Safety: Machine Learning to Predict Sensor-Related Accident Severity","volume":"12","author":"Shafique","year":"2024","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"El Hajj, M., and Hammoud, J. (2023). Unveiling the Influence of Artificial Intelligence and Machine Learning on Financial Markets. J. Risk Financ. Manag., 16.","DOI":"10.3390\/jrfm16100434"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"180","DOI":"10.56294\/ai2025180","article-title":"Enriching the tourist experience at the Santuario de las Lajas through image recognition using WhatsApp","volume":"4","author":"Melo","year":"2025","journal-title":"EthAIca"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"100470","DOI":"10.1016\/j.dajour.2024.100470","article-title":"A Systematic Review of Hyperparameter Optimization Techniques in Convolutional Neural Networks","volume":"11","author":"Raiaan","year":"2024","journal-title":"Decis. Anal. J."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e1484","DOI":"10.1002\/widm.1484","article-title":"Hyperparameter Optimization: Foundations, Algorithms, Best Practices, and Open Challenges","volume":"13","author":"Bischl","year":"2023","journal-title":"WIREs Data Min. Knowl. Discov."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Franceschi, L., Donini, M., Perrone, V., Klein, A., Archambeau, C., Seeger, M., Pontil, M., and Frasconi, P. (2025). Hyperparameter Optimization in Machine Learning. arXiv.","DOI":"10.1561\/2200000088"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Shekhar, S., Bansode, A., and Salim, A. (2021, January 8\u201310). A Comparative Study of Hyper-Parameter Optimization Tools. Proceedings of the 2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), Brisbane, Australia.","DOI":"10.1109\/CSDE53843.2021.9718485"},{"key":"ref_14","unstructured":"(2025, August 24). scikit-learn. RandomForestClassifier. Available online: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.ensemble.RandomForestClassifier.html."},{"key":"ref_15","unstructured":"Akinremi, B. (2025, August 24). Best Tools for Model Tuning and Hyperparameter Optimization. Neptune.ai. Available online: https:\/\/neptune.ai\/blog\/best-tools-for-model-tuning-and-hyperparameter-optimization."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.neucom.2020.07.061","article-title":"On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice","volume":"415","author":"Yang","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Elgeldawi, E., Sayed, A., Galal, A.R., and Zaki, A.M. (2021). Hyperparameter Tuning for Machine Learning Algorithms Used for Arabic Sentiment Analysis. Informatics, 8.","DOI":"10.3390\/informatics8040079"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"105992","DOI":"10.1016\/j.knosys.2020.105992","article-title":"Systematic Ensemble Model Selection Approach for Educational Data Zining","volume":"200","author":"Injadat","year":"2020","journal-title":"Knowl.-Based Syst."},{"key":"ref_19","unstructured":"Claesen, M., Simm, J., Popovic, D., Moreau, Y., and De Moor, B. (2014). Easy Hyperparameter Search Using Optunity. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Lorenzo, P.R., Nalepa, J., Kawulok, M., Ramos, L.S., and Pastor, J.R. (2017, January 15\u201319). Particle Swarm Optimization for Hyper-Parameter Selection in Deep Neural Networks. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO \u201917), Berlin, Germany.","DOI":"10.1145\/3071178.3071208"},{"key":"ref_21","first-page":"281","article-title":"Random Search for Hyper-Parameter Optimization","volume":"13","author":"Bergstra","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Witt, C. (2005, January 24\u201326). Worst-Case and Average-Case Approximations by Simple Randomized Search Heuristics. Proceedings of the 22nd Annual Conference on Theoretical Aspects of Computer Science (STACS\u201905), Stuttgart, Germany.","DOI":"10.1007\/978-3-540-31856-9_4"},{"key":"ref_23","unstructured":"Snoek, J., Larochelle, H., and Adams, R.P. (2012). Practical Bayesian Optimization of Machine Learning Algorithms. arXiv."},{"key":"ref_24","unstructured":"(2025, September 17). Hyperopt Documentation. Available online: https:\/\/hyperopt.github.io\/hyperopt\/."},{"key":"ref_25","first-page":"115","article-title":"Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures","volume":"28","author":"Bergstra","year":"2013","journal-title":"Proc. Mach. Learn. Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"103096","DOI":"10.1016\/j.cose.2023.103096","article-title":"A Comprehensive Study of DDoS Attacks over IoT Network and Their Countermeasures","volume":"127","author":"Kumari","year":"2023","journal-title":"Comput. Secur."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Miri Kelaniki, S., and Komninos, N. (2025). A Study on IoT Device Authentication Using Artificial Intelligence. Sensors, 25.","DOI":"10.20944\/preprints202508.0887.v1"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wahab, S.A., Sultana, S., Tariq, N., Mujahid, M., Khan, J.A., and Mylonas, A. (2025). A Multi-Class Intrusion Detection System for DDoS Attacks in IoT Networks Using Deep Learning and Transformers. Sensors, 25.","DOI":"10.3390\/s25154845"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Saito, T., and Rehmsmeier, M. (2015). The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0118432"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Chicco, D., and Jurman, G. (2020). The Advantages of the Matthews Correlation Coefficient (MCC) over F1 Score and Accuracy in Binary Classification Evaluation. BMC Genom., 21.","DOI":"10.1186\/s12864-019-6413-7"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Davis, J., and Goadrich, M. (2006, January 25\u201329). The Relationship between Precision-Recall and ROC Curves. Proceedings of the 23rd International Conference on Machine Learning (ICML \u201906), Pittsburgh, PA, USA.","DOI":"10.1145\/1143844.1143874"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"119330","DOI":"10.1016\/j.eswa.2022.119330","article-title":"Implementation of Intrusion Detection Model for DDoS Attacks in Lightweight IoT Networks","volume":"215","author":"Khanday","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"109277","DOI":"10.1016\/j.compeleceng.2024.109277","article-title":"A Robust DDoS Intrusion Detection System Using Convolutional Neural Network","volume":"117","author":"Najar","year":"2024","journal-title":"Comput. Electr. Eng."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"103347","DOI":"10.1016\/j.cose.2023.103347","article-title":"Edge-HetIoT Defense against DDoS Attack Using Learning Techniques","volume":"132","author":"Mahadik","year":"2023","journal-title":"Comput. Secur."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Ullah, S., Mahmood, Z., Ali, N., Ahmad, T., and Buriro, A. (2023). Machine Learning-Based Dynamic Attribute Selection Technique for DDoS Attack Classification in IoT Networks. Computers, 12.","DOI":"10.3390\/computers12060115"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Lv, H., Du, Y., Zhou, X., Ni, W., and Ma, X. (2023). A Data Enhancement Algorithm for DDoS Attacks Using IoT. Sensors, 23.","DOI":"10.3390\/s23177496"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Neto, E.C.P., Dadkhah, S., Ferreira, R., Zohourian, A., Lu, R., and Ghorbani, A.A. (2023). CICIoT2023: A Real-Time Dataset and Benchmark for Large-Scale Attacks in IoT Environment. Sensors, 23.","DOI":"10.20944\/preprints202305.0443.v1"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Sharafaldin, I., Lashkari, A.H., Hakak, S., and Ghorbani, A.A. (2019, January 1\u20133). Developing Realistic Distributed Denial of Service (DDoS) Attack Dataset and Taxonomy. Proceedings of the 2019 International Carnahan Conference on Security Technology (ICCST), Chennai, India.","DOI":"10.1109\/CCST.2019.8888419"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"100712","DOI":"10.1016\/j.eij.2025.100712","article-title":"Dynamic Oversampling-Driven Kolmogorov\u2013Arnold Networks for Credit Card Fraud Detection: An Ensemble Approach to Robust Financial Security","volume":"31","author":"Akouhar","year":"2025","journal-title":"Egypt. Inform. J."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Coello, C.A.C. (2011). Sequential Model-Based Optimization for General Algorithm Configuration. Learning and Intelligent Optimization, Springer.","DOI":"10.1007\/978-3-642-25566-3"},{"key":"ref_41","unstructured":"(2025, August 18). scikit-learn: Machine Learning in Python\u2014Scikit-Learn 1.7.2 Documentation. Available online: https:\/\/scikit-learn.org\/stable\/."}],"container-title":["Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2227-9709\/12\/4\/126\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T11:03:28Z","timestamp":1763636608000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2227-9709\/12\/4\/126"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,20]]},"references-count":41,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["informatics12040126"],"URL":"https:\/\/doi.org\/10.3390\/informatics12040126","relation":{},"ISSN":["2227-9709"],"issn-type":[{"value":"2227-9709","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,20]]}}}