AN EFFICIENT MACHINE LEARNING-BASED DETECTION AND PREDICTION MECHANISM FOR CYBER THREATS USING INTELLIGENT FRAMEWORK IN IOTS

Abstract

The dangers that Internet of Things (IoT) devices pose to large corporate corporations and smart districts have been dissected by several academics. Given the ubiquitous use of IoT and its unique characteristics, such as mobility and normalization restrictions, intelligent frameworks that can independently detect suspicious activity in privately linked IoT devices are crucial. The IoTs have led an explosion in traffic through the network, bringing information processing techniques for attack detection. The increase in traffic poses challenges in detecting attacks and differentiating traffic that is harmful. In this work, we have proposed a mechanism that uses the standard algorithms in a system that is designed to detect, track, measure and identify online traffic from organizations with malignant transmission: Random Forest (RF), gradient-boosted decision trees (GBDT), and support vector machines (SVM) gives an optimal accuracy of 80.34%,87.5%, and 88.6% while the random forest-based supervised approach is 5.5% better than the previous techniques. To facilitate comparisons between training time, prediction time, specificity, and accuracy, the proposed approach leverages the NSL KDD dataset accuracy.