Published Paper Details:

IoT security is the field of study concerned with the protection of linked devices and networks in the Internet of things (IoTs). It entails the rising frequency of things with unique IDs as well as the capacity to automatically transfer data over a network. Computing devices and embedded sensor systems used in industrial machine-to-machine (M2M) communications, smart energy grids, home and building automation, vehicle-to-vehicle communication, and wearable computing devices are driving most of the increase in IoT communication. The fundamental issue is that, because networking appliances and other items are still relatively new concepts, security has not always been considered in product design. IoT devices are frequently sold with outdated embedded operating systems and software. To strengthen security, an IoT device that requires direct Internet connectivity should be segregated into its own network and network access controlled. The network segment should then be watched to identify potential unusual traffic and, if necessary, action should be taken. The suggested method outlines the use of machine learning in IoT security systems to evaluate and learn from trends in order to help prevent similar assaults and respond to changing behaviour. Machine Learning can assist IoT security teams in being more proactive in preventing risks and responding in real time to active attacks.