ABSTRACT
The Internet of Things (IoT) platform has grown into a worldwide powerhouse in the previous decade, grabbing every area of our everyday lives with its unfathomable smart services that advance human life. IoT is now experiencing more security concerns than ever before because of its simple accessibility and rapid growth in demand for smart devices and networks. IoT can be protected by conventional security procedures. When it comes to advancing booms and diverse forms of attacks, standard methods are not as effective as they once were. The next generation of IoT systems requires a powerful, dynamically improved, continually updated security solution. A hidden spyware application intentionally inserts holes into a computer’s security. With the advent of machine learning (ML), deep learning (DL), numerous new research avenues are now open to address current and future issues in IoT. Smart devices and networks can benefit greatly from the use of ML and DL to detect and identify assaults and aberrant activity. This chapter highlights several IoT security approaches, including SDN-based, NFV-based, fog computing-based, edge computing-based, NFC protocol-based, SODA framework-based, and cross-layer security-based approaches, and provides some directions for future research.
