An Iot Based Machine Learning Technique For Efficient Online Load Forecasting

Document Type : Primary Research paper


1 Assistant Professor , PG & Research Department of Computer Science, Muthurangam Govt. Arts College (Autonomous), Otteri Road , Vellore - 632002. Vellore Dist, Tamil Nadu , India.

2 Professor, Department of Computer Science and Engineering, K.S.K College of Engineering and Technology, Kumbakonam, Tamilnadu-612702, India.

3 Assistant professor (Sl.G), Department of EEE, KPR Institute of Engineering and Technology,Coimbatore-641407.

4 Associate Professor, Department of Computer Science and Engineering,Presidency University, Bangalore-64.

5 Associate Professor, Department of Computer Science and Engineering, Velammal Institute of Technology, Velammal Gardens, Chennai – 601204.

6 Associate Professor ,Department of Computer Science & Engineering,Koneru Lakshmaiah Education Foundation (KLEF),Greenfields, Vaddeswaram, Guntur-522502.


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.