Use of Machine Learning in Detecting Network Security of Edge Computing System

Submitted by grigby1 on Tue, 01/28/2020 - 2:38pm

Title	Use of Machine Learning in Detecting Network Security of Edge Computing System
Publication Type	Conference Paper
Year of Publication	2019
Authors	Hou, Size, Huang, Xin
Conference Name	2019 IEEE 4th International Conference on Big Data Analytics (ICBDA)
ISBN Number	978-1-7281-1282-4
Keywords	Alibaba ECS, cloud computing, code mutation, composability, computer network security, edge computing, edge computing system, feature extraction, Hardware, home automation, Internet of Things, IoT systems, learning (artificial intelligence), machine learning, mutation code detection, network security detection, privacy, pubcrawl, radial basis function networks, RBF-function SVM method, resilience, Resiliency, smart home system, Smart homes, support vector machine, Support vector machines, Training
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.
URL	https://ieeexplore.ieee.org/document/8713237
DOI	10.1109/ICBDA.2019.8713237
Citation Key	hou_use_2019

Groups:

Science of Security VO