DS2AN: Deep stacked sparse autoencoder for secure and fast authentication in HetNets

Abstract

With the development of wireless communication technologies, providing seamless communication and secured roaming across devices has gained momentum. With 5G building upon and working together with 4G, it promises up to 100 times faster connectivity than 4G, greater bandwidth and extremely low latency. Though this dramatically enhances user experience, it calls for densification of the network by deploying smaller cells. This solution significantly improves network coverage but results in frequent handoffs. With the adoption of newer network technologies, potential new threats are introduced which becomes quite challenging for the industry. For ensuring seamless mobility, an effective handoff mechanism should ensure a secure and fast authentication process. In this article, Deep Sparse Stacked Autoencoder Network (DS2AN), an unsupervised deep learning model has been used to achieve the said objective. In the proposed method, training is done based on number of hidden layers using back propagation algorithm. Once trained, the cost function is minimized in order to achieve fine tuning of DS2AN.Compared with the existing handoff schemes, DS2AN promises a better level of security and a reduction in time required for handoff. Also, DS2AN is used to identify the presence of malicious entities thereby increasing security in the network. The performance of DS2AN algorithm for authentication delay has been evaluated based on communication and computational cost, and the results shows that DS2AN performs better compared to existing techniques.