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A New Efficient Method for the Detection of Intrusion in 5G and beyond Networks using ML

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Title A New Efficient Method for the Detection of Intrusion in 5G and beyond Networks using ML
 
Creator Yadav, Vikash
Rahul, Mayur
Yadav, Rishika
 
Subject Authentication
Cryptography
Machine Learning
Physical layer
Reliability
 
Description 60-65
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.
 
Date 2021-01-04T09:12:51Z
2021-01-04T09:12:51Z
2021-01
 
Type Article
 
Identifier 0975-1084 (Online); 0022-4456 (Print)
http://nopr.niscair.res.in/handle/123456789/55854
 
Language en_US
 
Rights CC Attribution-Noncommercial-No Derivative Works 2.5 India
 
Publisher NISCAIR-CSIR, India
 
Source JSIR Vol.80(01) [January 2021]