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IJSDR
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

Issue: March 2024

Volume 9 | Issue 3

Impact factor: 8.15

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Paper Title: PREDICTION OF SECURITY THREATS USING SUPERVISED MACHINE LEARNING TECHNIQUE
Authors Name: SHAIK DHEERAJ , SRINATH NADIPELLI , ARROJU MANOJ KUMAR , SAMEER PASHA MD , MS.S.SARJUN BEEVI
Unique Id: IJSDR2304353
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.
Keywords: Intrusion detection system , Random forest classification , Principle component analysis(PCA).
Cite Article: "PREDICTION OF SECURITY THREATS USING SUPERVISED MACHINE LEARNING TECHNIQUE", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.8, Issue 4, page no.2278 - 2291, April-2023, Available :http://www.ijsdr.org/papers/IJSDR2304353.pdf
Downloads: 000336257
Publication Details: Published Paper ID: IJSDR2304353
Registration ID:205419
Published In: Volume 8 Issue 4, April-2023
DOI (Digital Object Identifier):
Page No: 2278 - 2291
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631

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