Deep Learning Based Anomaly Network Detection and Data Management in IOT Sector

Abstract

one of the artificial intelligence's most promising and rapidly growing strategy is Deep Learning. The significant number of latest breakdown parameters in AI is under the responsibility of Dl, because of the superiority of DL models in recognizing trends from the results automatically. Nevertheless, profound training models depend heavily on the basic data. For a deep learning system, accuracy, precision and information completeness are important.This study aims at identifying the data management challenges facing beginners in various stages of start to end development and categorizing them.In this paper, a real time learning approach is used to investigate the issues of managing the data thatbeginners across different fields face when using case over data for education and the use of deep learning models. Our case study is aimed at providing the DL group as well as data inventors with valuable insights that will direct conversation and further invention in advanced education with case over data.