An Efficient Handover Authentication Mechanism Using Deep Learning in SDN-Based 5G HetNets.

The fifth generation (5G) networks are a popular standard that carries effective skills to conquer the tests of next generation wireless networks. Also, the 5G systems can support high data traffic by rendering high throughput and low latency towards the massively connected nodes. Here, handover is highly significant for data processing, portability and real time data creation in mobile technologies. With the 5G entrance, the cellular network has become a completely heterogeneous network (HetNet). The Software Defined Network (SDN) concept is used in 5G HetNets for better mobility management. Most existing research works have concentrated on handover authentication, but those works are often prone to re-authentication issues and increased handover delay. Therefore, to overcome the reauthentication process and provide users with better services, a novel handover authentication mechanism using deep learning (DHan_Auth) is proposed. Initially, the 5G data attack and normal data are collected, and the malicious and non-malicious users are classified using the Convolution Stacked long short term memory network model (Conv_SLSTM). To improve handover process and resist network attacks, only the non-malicious user data are authenticated through the key generation and the 5G Handover-Authentication and Key Agreement (5G_AKA) protocol. The process of encryption and decryption are performed using Extended Elliptic curve cryptography (Ex_ECC). When simulating with the PYTHON platform, performance such as handover latency, accuracy and precision are analyzed. The handover latency of the proposed model is 11.8 seconds to 200 nodes, while the classification accuracy in categorizing the malicious and non-malicious users reaches 98.98%. [ABSTRACT FROM AUTHOR]