Enhancing the Security of Iot Service Using Sem Model Based Machine Learning Technique

IoT platforms have been evolved over the last decade into a worldwide behemoth that is grabbing every part of our everyday lives by expanding human existence with its unaccountable smart services. This development occurred on a global scale. IoT is now experiencing greater security concerns than it ever has previously due to the ease with which it can be accessed and the rapidly increasing need for smart devices and networks. Existing security methods may be used to provide protection for the internet of things. Traditional methods, on the other hand, are not nearly as effective because of the technological booms as well as the many assault kinds and the severity of their effects. For this reason, the next-generation Internet of Things system has to include a security mechanism that is both robust and continually upgraded. The field of machine learning (ML) has recently seen a significant technical improvement, which has resulted in the opening of several prospective research opportunities to solve existing and future difficulties in the Internet of Things. To achieve this goal, ML is being utilized as a strong tool in order to detect attacks and identify unusual behaviors of smart devices and networks. This is being done in order to reach the target. This is being done in order to achieve the goal that has been set. Here, a SEM model has been proposed, which increases the overall performance of the ML classifiers that are employed as the basis of this structure. The SEM model is trained in such a manner that it helps to figure out the invasive behaviors that are present in a network that contains IoT devices. Our proposed method gave better results in comparison to other ML approaches having very less variation in performance parameters like accuracy, precision, TNR, FPR and F1 score.