Abstract
IoT comprises of devices connected to each other through the internet. Such IoT networks are now becoming easy prey for attackers. The attacks conducted can however be detected through the use of machine learning techniques. In this paper, Random Forest, Logistic Regression, Naive Bayes and Decision Tree machine learning algorithms are investigated in order to detect malicious traffic in an IoT network. The IoT-23 dataset is used. Best results were obtained using Decision tree
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
