eprintid: 17838 rev_number: 8 eprint_status: archive userid: 2 dir: disk0/00/01/78/38 datestamp: 2025-09-08 23:30:12 lastmod: 2025-09-08 23:30:14 status_changed: 2025-09-08 23:30:12 type: article metadata_visibility: show creators_name: Ali, Mudasir creators_name: Mushtaq, Muhammad Faheem creators_name: Akram, Urooj creators_name: Gavilanes Aray, Daniel creators_name: Masías Vergara, Manuel creators_name: Karamti, Hanen creators_name: Ashraf, Imran creators_id: creators_id: creators_id: creators_id: daniel.gavilanes@uneatlantico.es creators_id: manuel.masias@uneatlantico.es creators_id: creators_id: title: Botnet detection in internet of things using stacked ensemble learning model ispublished: pub subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: uninimx_produccion_cientifica divisions: uninipr_produccion_cientifica divisions: unic_produccion_cientifica divisions: uniromana_produccion_cientifica full_text_status: public keywords: Internet of things network, Botnets, Cyber security, Stacking model, Machine learning abstract: Botnets are used for malicious activities such as cyber-attacks, spamming, and data theft and have become a significant threat to cyber security. Despite existing approaches for cyber attack detection, botnets prove to be a particularly difficult problem that calls for more advanced detection methods. In this research, a stacking classifier is proposed based on K-nearest neighbor, support vector machine, decision tree, random forest, and multilayer perceptron, called KSDRM, for botnet detection. Logistic regression acts as the meta-learner to combine the predictions from the base classifiers into the final prediction with the aim of increasing the overall accuracy and predictive performance of the ensemble. The UNSW-NB15 dataset is used to train machine learning models and evaluate their effectiveness in detecting cyber-attacks on IoT networks. The categorical features are transformed into numerical values using label encoding. Machine learning techniques are adopted to recognize botnet attacks to enhance cyber security measures. The KSDRM model successfully captures the complex patterns and traits of botnet attacks and obtains 99.99% training accuracy. The KSDRM model also performs well during testing by achieving an accuracy of 97.94%. Based on 3, 5, 7, and 10 folds, the k-fold cross-validation results show that the proposed method’s average accuracy is 99.89%, 99.88%, 99.89%, and 99.87%, respectively. Further, the demonstration of experiments and results shows the KSDRM model is an effective method to identify botnet-based cyber attacks. The findings of this study have the potential to improve cyber security controls and strengthen networks against changing threats. date: 2025-07 publication: Scientific Reports volume: 15 number: 1 id_number: doi:10.1038/s41598-025-02008-9 refereed: TRUE issn: 2045-2322 official_url: http://doi.org/10.1038/s41598-025-02008-9 access: open language: en citation: Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Internacional Iberoamericana Puerto Rico > Investigación > Artículos y libros Universidad Internacional do Cuanza > Investigación > Producción Científica Universidad de La Romana > Investigación > Producción Científica Abierto Inglés Botnets are used for malicious activities such as cyber-attacks, spamming, and data theft and have become a significant threat to cyber security. Despite existing approaches for cyber attack detection, botnets prove to be a particularly difficult problem that calls for more advanced detection methods. In this research, a stacking classifier is proposed based on K-nearest neighbor, support vector machine, decision tree, random forest, and multilayer perceptron, called KSDRM, for botnet detection. Logistic regression acts as the meta-learner to combine the predictions from the base classifiers into the final prediction with the aim of increasing the overall accuracy and predictive performance of the ensemble. The UNSW-NB15 dataset is used to train machine learning models and evaluate their effectiveness in detecting cyber-attacks on IoT networks. The categorical features are transformed into numerical values using label encoding. Machine learning techniques are adopted to recognize botnet attacks to enhance cyber security measures. The KSDRM model successfully captures the complex patterns and traits of botnet attacks and obtains 99.99% training accuracy. The KSDRM model also performs well during testing by achieving an accuracy of 97.94%. Based on 3, 5, 7, and 10 folds, the k-fold cross-validation results show that the proposed method’s average accuracy is 99.89%, 99.88%, 99.89%, and 99.87%, respectively. Further, the demonstration of experiments and results shows the KSDRM model is an effective method to identify botnet-based cyber attacks. The findings of this study have the potential to improve cyber security controls and strengthen networks against changing threats. metadata Ali, Mudasir; Mushtaq, Muhammad Faheem; Akram, Urooj; Gavilanes Aray, Daniel; Masías Vergara, Manuel; Karamti, Hanen y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, daniel.gavilanes@uneatlantico.es, manuel.masias@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2025) Botnet detection in internet of things using stacked ensemble learning model. Scientific Reports, 15 (1). ISSN 2045-2322 document_url: http://repositorio.unib.org/id/eprint/17838/1/s41598-025-02008-9.pdf