| Abstract |
Over the past few years, smart cities have seamlessly integrated into our daily lives, offering convenience and simplicity. However, as these cities become increasingly interconnected and reliant on the Internet of Things (IoT), ensuring heightened security measures becomes paramount. The potential compromise of IoT devices due to vulnerabilities poses significant risks, including the theft of personal data, leading to severe hazards for individuals. Thus, Security plays a pivotal role in safeguarding IoT devices. In this modern era, integrating security measures with machine learning has emerged as a solution to automate and streamline security protocols. This requires a comprehensive analysis of enhancing security levels in IoT devices within innovative city environments. Our study extensively surveys security issues across various facets of IoT infrastructure, including hardware, cloud environments, applications, data, software, and networks. Through thorough examination, we identify the effects of these issues and propose countermeasures to bolster Security, mainly focusing on IoT devices. Furthermore, our study delves into various machine learning algorithms, providing examples, detailing attack types, and assessing accuracy rates for each algorithm. We offer a quick reference guide that outlines the benefits and drawbacks of different machine-learning algorithms and their applications. Additionally, we aim to identify and mitigate various security threats by exploring diverse datasets, evaluation metrics, IoT threats, and machine-learning techniques. By thoroughly exploring these aspects, our study equips future researchers with the knowledge to effectively identify potential security threats and implement robust safeguards against them. |