  Over the last decade, IoT platforms have been developed into a global giant
that grabs every aspect of our daily lives by advancing human life with its
unaccountable smart services. Because of easy accessibility and fast-growing
demand for smart devices and network, IoT is now facing more security
challenges than ever before. There are existing security measures that can be
applied to protect IoT. However, traditional techniques are not as efficient
with the advancement booms as well as different attack types and their
severeness. Thus, a strong-dynamically enhanced and up to date security system
is required for next-generation IoT system. A huge technological advancement
has been noticed in Machine Learning (ML) which has opened many possible
research windows to address ongoing and future challenges in IoT. In order to
detect attacks and identify abnormal behaviors of smart devices and networks,
ML is being utilized as a powerful technology to fulfill this purpose. In this
survey paper, the architecture of IoT is discussed, following a comprehensive
literature review on ML approaches the importance of security of IoT in terms
of different types of possible attacks. Moreover, ML-based potential solutions
for IoT security has been presented and future challenges are discussed.
