IoT Security Challenges and Their Solutions: An Analysis Using AI and Machine Learning

The Internet of Things (IoT) has completely changed how we interact with technology, bringing more connectedness and ease into our daily lives. However, because IoT devices provide fresh and challenging security issues, This increasing connectivity also increases the potential of cyber assaults. With the help of artificial intelligence (AI) and machine learning (ML), This research paper addresses the key IoT security issues, such as authentication, data privacy, and device tampering, and makes recommendations for solutions. IoT security measures can be greatly improved by AI and ML by examining behaviour patterns, identifying weaknesses, and foreseeing possible threats. This research paper cover current IoT security assaults and looks at how AI and ML might have been able to stop them. It also looks at the current limitations and potential future directions of AI and ML in IoT security. The study concludes that although AI and ML are promising technologies for addressing IoT security concerns, they must be utilised in conjunction with other security measures, and continued innovation and investment in IoT security is important for a safer and more secure futureThe limitations of conventional security measures will then be discussed, and possible solutions to these problems will be suggested using AI and machine learning (ML). The analysis of AI and ML applications in IoT security will include threat prediction, intrusion detection, and anomaly detection, among other topics. Additionally, it will look at how AI and ML may be used to protect IoT networks from Distributed Denial of Service (DDoS) assaults, which have been more frequent in recent years. The article will show case studies of recent assaults on IoT devices and networks, like the Mirai botnet attack in 2016, and look at how AI and ML may have been able to avoid or mitigate these attacks. The paper will also discuss the difficulties and restrictions associated with using AI and ML to IoT security, such as the requirement for huge datasets and computing capacity as well as the possibility of bias in machine learning algorithms. The paper will finish by summarising the potential advantages of employing AI and ML in IoT security, such as enhanced accuracy and efficiency in threat detection, and the necessity for continued investment in research and development to address the quickly changing IoT security scenario.