Advancing IoT Network Security: A Comparative Study with Advanced Machine Learning Methods

Intrusion Detection Systems of IoT networks have undergone a progressive evolution of making the model more complex, and hence, the use of ensemble learning for enhanced detection has been investigated. In this paper, the evaluation of various classifiers have been applied in facing the emerging issues within IoT security. Each and every approach has its own unique approach indealing real time problems. In this paper, we proposed extreame gradient boosting classifier to classify the attaks in RT_IoT data. Rather that the individual machine learning models, Integrating the individual machine learning classifiers, XG-Boost is far better than standalone traditional techniques, especially for dimensional data of IoT nature. Crossmodel comparisons show that ensemble techniques outperform baseline models, including boosting and bagging, to be more accurate, less prone to overfitting, and more resilient to adversarial manipulation. Using the example of XG-Boost as a common classifier for ensemble learning, this research underlines the prospects of this concept in helping IoT IDS develop better at identifying complex intrusion patterns to become a valuable means of securing IoT networks.