Published November 25, 2022 | Version v1
Conference paper Open

IoT Network Intrusion Detection with Ensemble Learners

  • 1. University of Surrey
  • 2. Technical University Chemnitz


Protecting information systems against intruders’ attacks requires utilising intrusion detection systems. Over the past several years, many open-source intrusion datasets have been made available so that academics and researchers can analyse and assess various detection classifiers’ effectiveness. These datasets are made available with a full complement of illustrative network features. In this research, we investigate the issue of Network Intrusion Detection (NID) by utilising an Internet of Things (IoT) dataset called Bot-IoT to evaluate the detection efficiency and effectiveness of five different Ensemble Learning Classifiers (ELCs). Our experiment’s results showed that despite all ELCs recording high classification metric scores, CatBoost emerged as the ELC that performed the best in our experiment in terms of Accuracy, Precision, F1-Score, Training and Test Time.



Files (214.4 kB)

Additional details


DEDICAT 6G – Dynamic coverage Extension and Distributed Intelligence for human Centric applications with assured security, privacy and trust: from 5G to 6G 101016499
European Commission
5G-HEART – 5G HEalth AquacultuRe and Transport validation trials 857034
European Commission
L3Pilot – Piloting Automated Driving on European Roads 723051
European Commission