Correction: Boostedenml: Efficient Technique for Detecting Cyberattacks in Iot Systems Using Boosted Ensemble Machine Learning (Sensors, 2022 22 19 7409 10.3390/S22197409) - Adasme-Soto, P (Multidisciplinary Digital Publishing Institute (MDPI), 2025)
There was an error in the original publication [1]. A correction has been made due to an error in the presentation of the percentage values in paragraph 13 in the Section 4 Results and Discussion. In terms of the F-score, which is the weighted mean of the recall and precision of the model behavior, Table 8 demonstrates that the EnsHMV reached 96.36%, 99.84%, 99.99%, 99.89%, 98.90%, 99.69%, 100%, 99.5%, 99.92%, and 99.88% in classifying the benign, botnet, brute force, DDoS, DoS, heartbleed, infiltration, portscan, and web attack traffics in the CICIDS2017 dataset, respectively, while on the CSE-CICIDS2018 dataset, the EnsHMV attained an F-score performance of 99.78%, 100%, 100%, 100%, 99.99%, 99.99%, and 100% in classifying the benign, botnet, brute force, DDoS, DoS, infiltration, and web attack flows, respectively. Similarly, the BoostedEnML showed higher performance than the EnsHMV in relation to the F-score measure on both datasets. Specifically, on the CICIDS2017 dataset, the BoostedEnML showed an F-score of 100% in the classification of the benign, botnet, brute Force, DDoS, Dos, heartbleed, infiltration, portscan, and web attack flows. It also achieved 100% in detecting the benign, botnet, brute force, DDoS, DoS, infiltration, and web attack packets in the CSE-CICIDS2018 dataset. In the original publication, there was a mistake in the data in Table 8. The correct data appears below. The authors state that the scientific conclusions are unaffected. This correction was approved by the Academic Editor. The original publication has also been updated. Performance of the IDS models (EnsHMV and BoostedEnsML) in detecting and classifying each network traffic class in the two datasets. © 2025 by the authors.