INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15
PREDICTION OF NETWORK ATTACKS USING SUPERVISED MACHINE LEARNING TECHNIQUE
Authors Name:
ABBURI SAMPATH KUMAR
, TALLURI S S SAI KRISHNA , THELLAGORLA RAMGOPI , DUDEKULA MABU RAMJAN , DR. C. RAJABHUSHANAM
Unique Id:
IJSDR2304398
Published In:
Volume 8 Issue 4, April-2023
Abstract:
With the development of wireless communications at the Internet, there are numerous security threats. An Intrusion Detection System (IDS) helps stumble on attacks on a system and discover intruders. Previously, numerous gadget learning strategies (ML) were used in IDS strategies which have attempted to enhance intruder detection consequences and improve the accuracy of IDS. This article presents an approach to imposing an IDS the usage of Principal Component Analysis (PCA) and a random forest type set of rules. Where PCA will assist to arrange the information with the aid of lowering the dimensionality of the data and Random Forests will assist inside the type. The outcomes obtained show that the proposed approach plays extra efficiently in terms of accuracy compared to other strategies, which include SVM, Naïve Bayes and Decision Tree. The results received via the proposed approach have values for the length (min) of 3.24 mins, accuracy (%) of 96.78% and accuracy (%) of 0.21%.
Keywords:
Machine Learning (ML), classification method, python, Prediction of Accuracy result.
Cite Article:
"PREDICTION OF NETWORK ATTACKS USING SUPERVISED MACHINE LEARNING TECHNIQUE", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.8, Issue 4, page no.2579 - 2585, April-2023, Available :http://www.ijsdr.org/papers/IJSDR2304398.pdf
Downloads:
000337209
Publication Details:
Published Paper ID: IJSDR2304398
Registration ID:205788
Published In: Volume 8 Issue 4, April-2023
DOI (Digital Object Identifier):
Page No: 2579 - 2585
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631
Facebook Twitter Instagram LinkedIn