Machine Learning-Based Solutions for Internet of Things (IoT) and Cybersecurity

In the previous decade, Internet of Things (IoT) systems have grown into a worldwide behemoth that has encompassed every element of the everyday existence by enhancing human existence with uncountable intelligent assistance. Due to the ease of usage and increasing need for smart gadgets and networks, IoT is today experiencing more cybersecurity concerns than ever before. As a result, for contemporary IoT systems, a powerful, constantly improved, and current cybersecurity solution is necessary. A significant technological improvement in Machine Learning (ML) has been observed, opening up several potential study avenues for tackling existing and prospective IoT concerns. The fundamental goal of this study is to implement ML-based solution for IoT cybersecurity. In the initial phase of this study approach, feature scaled has been performed on the UNSW-NB15 database utilizing the Minimum-maximum idea of normalizing to reduce data leaks on the experimental statistics. Principal Components Assessment (PCA) have been utilized to reduce dimensions in the following phase. Finally, for the investigation, 6 suggested ML solutions have been applied. The outcomes from experiments have been assessed by means of validating database. The outcomes have been compared to previous research, and the outcomes have been compatible with an accuracy of 99.99 percent and an MCC-Mathew correlation coefficient of 99.97 percent.