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.
Internet of Things (IoT), Device Authentication, Machine Learning, Security.