IOT & Machine Learning Models to Develop The Architecture For Assessment Of Security Threats.

The Internet of Things (IoT) and other recent advancements in communication and information technology have profoundly impacted and enhanced people’s quality of life. IoT systems are vulnerable to previously unanticipated cyber threats such as denial of service, jamming, phishing, obfuscations eavesdropping, spoofing and invasions because of the widespread availability and rising demand for connected devices. The emerging cyber-physical security problem is difficult to avoid and guard against using conventional methods. Securing IoT systems calls for robust, dynamic, and current security mechanisms. When addressing emerging security concerns in cyber-physical systems (CPS), machine learning (ML) technology is often regarded as the most cutting- edge and promising option. This literature review explains how IoTs are built, looks into the many threats they face, and discusses the most up-to-date efforts being made to ensure their security using machine learning. In addition, it covers some of the research obstacles that may arise in the future while trying to implement security measures in IoT infrastructure.