Intrusion Detection System For IoT Networks Using Neural Networks With Extended Kalman Filter

Kulkarni, Divya D.; Rathore, Shruti; Jaiswal, Raj Kumar

doi:10.1109/icccn52240.2021.9522335

Cited by 5 publications

(1 citation statement)

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“…In some research, neither the classification task type nor the experiment duration is specified. In addition, we discovered that most research evaluating the effectiveness of various ML classifiers on the Bot-IoT dataset employed the binary or 5-class category [21]- [23]. In this work, we apply multiple ELCs to classify the Bot-IoT dataset instances to assess their classification effectiveness alongside their training and test time.…”

Section: Related Workmentioning

confidence: 99%

IoT Network Intrusion Detection with Ensemble Learners

Abdulkareem

Foh

Lee

et al. 2022

2022 13th International Conference on Information and Communication Technology Convergence (ICTC)

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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.

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Section: Related Workmentioning

confidence: 99%