An Efficient Machine Learning Techniques Based on IoT for Effective Load Forecasting
K. Rameshwaraiah, Sankarpu Sateesh Kumar, K. Srinivasa Babu, T. Madhu · IOP Conference Series Materials Science and Engineering · 2021
Abstract Internet of Things (IoT) networks (IoT) are computer networks which have an acute IT protection problem and in particular a computer attack detection problem. In order to solve this issue, the paper recommends the combination of machine learning approaches and concurrent data processing. The framework is developed and a new approach to the combination of the main classifiers intended for attacks on IoT networks. In which the accuracy ratio to the training time is the integral measure of efficacy, the problem classification statement is developed. We recommend the use of the data processing and multithreaded mode provided by Spark to accelerate the speed of training and testing. In addition, a technique is suggested for preprocessing data set, which results in a large reduction in the sample length. An experimental examination of the proposed approach reveals that the precision of IoT networks’ attack detection is 100 percent and the processing speed of data sets increases proportionally to the number of parallel threads.