Abstract:
Internet of Things (IoT) devices are interconnected devices that integrate things
and the Internet to make human life easy and faster and also Internet of Things
devices are interconnected for a longer period without human intervention. This
raises to develop security solutions to handle the security issues in the IoT network
which is compatible with the services.
In this research, we used raw data to construct the model for the system and after
the data is prepared there are different mechanisms that we follow to analyze the
data; data pre-processing for removing the irrelevant feature in the data, feature
selection for selecting features using random forest algorithm.
We conduct our experiments by selecting four different supervised machine learning for the classification of attacks on the IoT network. From the experimental
result cascading two machine learning algorithms (Random forest and Support
Vector Machine) performance is better than among other cascading machine learning algorithms.
