Volume 11 - Volume 11
An IoT based Machine Learning Technique for Efficient Online Load Forecasting
Abstract
Internet of Things (IoT) networks are computer networks that have an extreme issue with IT security
and an issue with the monitoring of computer threats in specific. The paper proposes a combination
of machine learning methods and parallel data analysis to address this challenge. The architecture
and a new approach to the combination of the key classifiers intended for IoT network attacks are
being developed. The issue classification statement is created in which the consistency ratio to
training time is the integral measure of effectiveness. To improve the preparation and assessment
pace, it is suggested to use the data processing and multi-threaded mode offered by Spark. In comparison, a preprocessing data set approach is proposed, resulting in a significant reduction in
the length of the sample. An experimental review of the proposed approach reveals that the precision of IoT network attack detection is 100%, and the processing speed of the data collection
increases with the number of parallel threads.
Paper Details
PaperID: 1686
Author's Name: B. Madhuravani, Srujan Atluri and Hema Valpadasu
Volume: Volume 11
Issues: Volume 11
Keywords: Design of Classifier, Parallel Processing, ML (Machine Learning) and Evaluation
Year: 2021
Month: April
Pages: 547-554