Machine Learning-Based Cybersecurity Framework for IoT Devices

Due to their low processing power, disparate operating systems, and frequently reticent security mechanisms, Internet of Things gadgets create unique security difficulties. These elements come together to provide special security challenges for the Internet of Things. This is specifically because they are becoming less common in artificial and consumer activities. The architecture that has been provided uses sophisticated machine literacy techniques to identify and eliminate a variety of cybersecurity issues, including malware attacks, unauthorised access, and data breaches. The framework can be used to recognise and remove these hazards. The frame detects anomalies and hidden dangers in real time by continuously monitoring network activity and device gesture. This is a big plus since it makes it possible to design creative protection mechanisms. Experiments have shown that the technique is useful for improving the security of networks linked to the Internet of Things (IoT) without appreciably impacting device performance. The trials' outcomes served as evidence for this. The investigation's conclusions provide insight into the potential for fusing cybersecurity tactics with machine literacy to solve issues that are spreading throughout the Internet of Things' geographic domain. This would offer a dependable solution that would protect confidential information and ensure that connected systems would carry on as usual until further notice.