A New Efficient Method for the detection of intrusion in 5 G andbeyond Networks using Machine Learning
The 5G networks are very important to support complex application byconnecting different types of machines and devices, which provide the platform for differentspoofing attacks. Traditional physical layer and cryptography authentication methods arefacing problems in dynamic complex environment, including less reliability, securityoverhead also problem in predefined authentication system, giving protection and learn abouttime-varying attributes. In this paper, intrusion detection framework has been designed usingvarious machine learning methods with the help of physical layer attributes and to providemore efficient system to increase the security. Machine learning methods for the intelligentintrusion detection are introduced, especially for supervised and non-supervised methods.Our machine learning based intelligent intrusion detection technique for the 5G and beyondnetworks is evaluated in terms of recall, precision, accuracy and f-value are validated forunpredictable dynamics and unknown conditions of networks.
Cryptography; Machine learning; Physical layer; Reliability; Authentication
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