  The integration of the Internet of Things (IoT) connects a number of
intelligent devices with a minimum of human interference that can interact with
one another. IoT is rapidly emerging in the areas of computer science. However,
new security problems were posed by the cross-cutting design of the
multidisciplinary elements and IoT systems involved in deploying such schemes.
Ineffective is the implementation of security protocols, i.e., authentication,
encryption, application security, and access network for IoT systems and their
essential weaknesses in security. Current security approaches can also be
improved to protect the IoT environment effectively. In recent years, deep
learning (DL)/ machine learning (ML) has progressed significantly in various
critical implementations. Therefore, DL/ML methods are essential to turn IoT
systems protection from simply enabling safe contact between IoT systems to
intelligence systems in security. This review aims to include an extensive
analysis of ML systems and state-of-the-art developments in DL methods to
improve enhanced IoT device protection methods. On the other hand, various new
insights in machine and deep learning for IoT Securities illustrate how it
could help future research. IoT protection risks relating to emerging or
essential threats are identified, as well as future IoT device attacks and
possible threats associated with each surface. We then carefully analyze DL and
ML IoT protection approaches and present each approach's benefits,
possibilities, and weaknesses. This review discusses a number of potential
challenges and limitations. The future works, recommendations, and suggestions
of DL/ML in IoT security are also included.
