@inproceedings{7ad2da9469384490935bff77e353b362,
title = "Use of Machine Learning in Detecting Network Security of Edge Computing System",
abstract = "This study has built a simulation of a smart home system by the Alibaba ECS. The architecture of hardware was based on edge computing technology. The whole method would design a clear classifier to find the boundary between regular and mutation codes. It could be applied in the detection of the mutation code of network. The project has used the dataset vector to divide them into positive and negative type, and the final result has shown the RBF-function SVM method perform best in this mission. This research has got a good network security detection in the IoT systems and increased the applications of machine learning.",
keywords = "Internet of things, code mutation, edge computing, machine learning, support vector machine",
author = "Size Hou and Xin Huang",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 4th IEEE International Conference on Big Data Analytics, ICBDA 2019 ; Conference date: 15-03-2019 Through 18-03-2019",
year = "2019",
month = may,
day = "10",
doi = "10.1109/ICBDA.2019.8713237",
language = "English",
series = "2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "252--256",
booktitle = "2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019",
}