Utilizing Machine Learning Algorithms to Improve Device Authentication in IoT

The increase in the use of IoT (Internet Of Things) gadgets in several sectors such as smart homes, agriculture, cities, and health has resulted in a growth in security challenges, notably authentication. Authentication is a security mechanism that creates the difference between legitimate and illegitimate users, and it also encompasses the identification and verification of the users. Device authentication is a challenge in IoT setting since IoT devices are resource constraint in their make and often uses passwords set by the manufacturer. Normally end users of the IoT devices do not change the passwords which make the smart environment prone to hackers. This study is aimed at using machine learning algorithms to detect and verify IoT devices in a smart home network. Only legitimate users are to have access to use the network resources. The results show that the approach has a 96% accuracy of classifying devices based on supervised machine learning algorithms and an illegitimate device can be blocked