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Intrusion Detection on NF-BoT-IoT Dataset Using Artificial Intelligence Techniques
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Author(s): G. Aarthi (B.S. Abdur Rahman Crescent Institute of Science and Technology, India), S. Sharon Priya (B.S. Abdur Rahman Crescent Institute of Science and Technology, India)and W. Aisha Banu (B.S. Abdur Rahman Crescent Institute of Science and Technology, India)
Copyright: 2023
Pages: 18
Source title:
Handbook of Research on Deep Learning Techniques for Cloud-Based Industrial IoT
Source Author(s)/Editor(s): P. Swarnalatha (Department of Information Security, School of Computer Science and Engineering, Vellore Institute of Technology, India)and S. Prabu (Department Banking Technology, Pondicherry University, India)
DOI: 10.4018/978-1-6684-8098-4.ch007
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Abstract
The rapid development of internet of things (IoT) applications has created enormous possibilities, increased our productivity, and made our daily life easier. However, because of resource limitations and processing, IoT networks are open to number of threats. The network instruction detection system (NIDS) aims to provide a variety of methods for identifying the increasingly common cyberattacks (such as distributed denial of service [DDoS], denial of service [DoS], theft, etc.) and to prevent hazardous activities. In order to determine which algorithm is more effective in detecting network threats, multiple public datasets and different artificial intelligence (AI) techniques are evaluated. Some of the learning algorithms like logistic regression, random forest, decision tree, naive bayes, auto-encoder, and artificial neural network were analysed and concluded on the NF-BoT-IoT dataset using various evaluation metrics. In order to train the model for future anomaly detection prediction and analysis, the feature extraction and pre-processing data were then supplied into NIDS as data.
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