An optimization-based machine learning technique for smart home security using 5G

Vidhu Kiran Sharma

doi:10.1016/j.compeleceng.2022.108434

An optimization-based machine learning technique for smart home security using 5G

2022-12-01

6

PRE

AI

V

Vidhu Kiran Sharma

S

Srikanta Kumar Mohapatra

DOI：10.1016/j.compeleceng.2022.108434

Original

Original request for help

Save

Abstract

En 中文

Generally, cellular networks are divided into discrete geographic zones where a secure routing protocol is important. In this study, Sailfish-based Distributed IP Mobility Management (SbDMM) architecture for security protocol in a smart home using 5G is suggested. Smart homes first gathered data via IoT devices which are then communicated with the use of a Home Gateway (HGW). Mobile Nodes (MN) and Corresponding Nodes (CN) process data communication (CN). In addition, the acquired data are encrypted and secured using the session key. Additionally, use an authenticated key and a cipher key to secure the routing optimization. As a result, the fitness of sailfish is updated in a protocol path that is optimized for securing data from attackers. The designed framework is then implemented in Python and the obtained results are compared to those of other methodologies in terms of execution time, confidentiality rate, efficiency, delay, and task completion.

Keywords:

Mobile node

Secure routing

Cipher key

Smart homes

Mobility

IoT application

5G network

Data transmission

An optimization-based machine learning technique for smart home security using 5G

Abstract

Journal

Organization