Özet:

Abstract The Internet of Things (IoT) will continue to have an increasing impact on our economic, commercial, and social lives. Because they frequently have limited resources, IoT network nodes become appealing targets for hackers. In order to address the security and privacy challenges that IoT networks face, a lot of work has been put forth, primarily using conventional encryption approaches. IoT networks have a number of security issues, but current solutions are limited by the characteristics of IoT nodes. It can be difficult to have secure and private conversations due to the Internet of Things' (IoT) extensive use and implementation. Different security concerns in IoT networks and devices can be addressed using machine learning (ML) techniques. This proposal offers a comprehensive examination of the security requirements for IoT networks, potential attack vectors, and current security measures. In order to address new security issues in cyber-physical systems (CPS), a number of research directions have been investigated. One of these directions is machine learning (ML), which has been hailed as the most cutting-edge and promising strategy. It addresses the most recent developments in machine learning techniques to address IoT device security challenges, describes IoT system designs, and analyzes various IoT system assaults. The use of machine learning techniques to offer decentralized privacy and protection was recently proposed. This proposal also discusses potential research challenges brought on by IoT devices' potential use of security measures in the future.

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