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INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH
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ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
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Paper Title:	NETWORK INTRUSION DETECTION USING PCA WITH RANDOM FOREST
Authors Name:	Kusalatha , Bhanu Prasad M.C
Unique Id:	IJSDR2303195
Published In:	Volume 8 Issue 3, March-2023
Abstract:	With the development of wi-fi communications at the Internet, there are numerous protection threats. An Intrusion Detection System (IDS) allows stumble on assaults on a gadget and discover intruders. Previously, diverse device studying (ML) techniques have been carried out to IDS which have tried to improve intruder detection outcomes and enhance the accuracy of IDS. This article proposes an method to implement IDS the use of Principal Component Analysis (PCA) and a random forest class set of rules. Where PCA will help to organize the records via decreasing the dimensionality of the facts and Random Forests will help within the category. The effects acquired show that the proposed method is more green in phrases of accuracy as compared to other techniques inclusive of SVM, Naïve Bayes and Decision Tree. The effects obtained by means of the proposed technique have values for the duration (min) of 3.24 mins, accuracy (%) of 96.78% and mistakes (%) of 0.21%.
Keywords:	MACHINE LEARNING PCA
Cite Article:	"NETWORK INTRUSION DETECTION USING PCA WITH RANDOM FOREST", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.8, Issue 3, page no.1189 - 1197, March-2023, Available :http://www.ijsdr.org/papers/IJSDR2303195.pdf
Downloads:	000337070
Publication Details:	Published Paper ID: IJSDR2303195 Registration ID:204711 Published In: Volume 8 Issue 3, March-2023 DOI (Digital Object Identifier): Page No: 1189 - 1197 Publisher: IJSDR \| www.ijsdr.org ISSN Number: 2455-2631

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