Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/55854
Title: A New Efficient Method for the Detection of Intrusion in 5G and beyond Networks using ML
Authors: Yadav, Vikash
Rahul, Mayur
Yadav, Rishika
Keywords: Authentication;Cryptography;Machine Learning;Physical layer;Reliability
Issue Date: Jan-2021
Publisher: NISCAIR-CSIR, India
Abstract: The 5G networks are very important to support complex application by connecting different types of machines and devices, which provide the platform for different spoofing attacks. Traditional physical layer and cryptography authentication methods are facing problems in dynamic complex environment, including less reliability, security overhead also problem in predefined authentication system, giving protection and learn about time-varying attributes. In this paper, intrusion detection framework has been designed using various machine learning methods with the help of physical layer attributes and to provide more efficient system to increase the security. Machine learning methods for the intelligent intrusion detection are introduced, especially for supervised and non-supervised methods. Our machine learning based intelligent intrusion detection technique for the 5G and beyond networks is evaluated in terms of recall, precision, accuracy and f-value are validated for unpredictable dynamics and unknown conditions of networks.
Page(s): 60-65
URI: http://nopr.niscair.res.in/handle/123456789/55854
ISSN: 0975-1084 (Online); 0022-4456 (Print)
Appears in Collections:JSIR Vol.80(01) [January 2021]

Files in This Item:
File Description SizeFormat 
JSIR 80(1) 60-65.pdf496.4 kBAdobe PDFView/Open


Items in NOPR are protected by copyright, with all rights reserved, unless otherwise indicated.