NETWORK INTRUSION DETECTION USING PCA WITH RANDOM FOREST
Kusalatha
, Bhanu Prasad M.C
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%.
"NETWORK INTRUSION DETECTION USING PCA WITH RANDOM FOREST", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.8, Issue 3, page no.1189 - 1197, March-2023, Available :https://ijsdr.org/papers/IJSDR2303195.pdf
Volume 8
Issue 3,
March-2023
Pages : 1189 - 1197
Paper Reg. ID: IJSDR_204711
Published Paper Id: IJSDR2303195
Downloads: 000347183
Research Area: Engineering
Country: KANCHIPURAM, TAMILNADU, INDIA
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
