Efficient Cyber Security Framework for IoT Using Machine Learning Algorithms

Abstract

Through the network's infrastructure, the IoT can impart perception, recognition, and remote control to inanimate objects. Due to IoT's characteristics, it's possible to integrate the real world into a digital system, which improves precision, productivity, and bottom line. While traditional internet infrastructure comprises of highly capable computers and servers, IoT gadgets have limited processing power and storage space. The authentication and key agreement system SecureAuthKey is very lightweight. The suggested technique is meant to solve the security and privacy problems that plague modern constraint-based CPS programmes. A lightweight approach for authenticating cyber-physical objects is one of the expected outcomes. Adaptation and autonomy in cyber-physical systems need not be compromised by a lack of trust or security in users' data or the devices themselves, according to a new type of security algorithm. To provide flexibility and scalability, a new middleware module has been developed on the Raspberry platform to facilitate communication between CPS-based devices and services. Secure Internet of Things (IoT) evaluation methodology that may be used in a variety of contexts and is user-focused. With the suggested system, the two CPS units will be able to authenticate one another. When a network grows larger, the possibility of an attack rises. Therefore, the IoT network is much more susceptible to attacks than conventional networks. As the number of connected devices grows, so do the number of potential threats to their security. To protect the IoT ecosystem from current threats, cutting-edge technology is essential. The proposed approach must be very efficient and have low computational overheads. It creates random session keys to protect wireless transmissions. The system is protected from a variety of cyber threats. The current constraint-based CPS system requires a new lightweight security solution.