A Comparative Analysis of Malicious Traffic Detection in IoT Network using Machine Learning Algorithms

To monitor and control unwanted traffic flows in the Internet of Things (IoT) network, it's essential differentiate between suspicious and malicious traffic. The use of ML (Machine Learning) approach models to avoid dangerous traffic flows has been shown in the Internet Things network. To set acceptance criteria for reliable malicious traffic detection in an IoT network, the issue has to be looked at. To solve the problem, we created a hybrid model that incorporates elements of artificial neural networks and logical regression.