MECGuard: GRU enhanced attack detection in Mobile Edge Computing environment

With the commercialization of 5G technology, various Mobile Edge Computing (MEC) services are being deployed widely. Generally, MEC services rely on MEC devices and servers deployed at the edge of the network. Whether it is a MEC device or an edge server, most of them lack computing resources, and i...

Full description

Saved in:
Bibliographic Details
Published in:Computer communications 2021-04, Vol.172, p.1-9
Main Authors: Liu, Xin, Zhang, Wenqiang, Zhou, Xiaokang, Zhou, Qingguo
Format: Article
Language:English
Subjects:
Anomaly detection
Edge computing security
Gated recurrent unit
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:With the commercialization of 5G technology, various Mobile Edge Computing (MEC) services are being deployed widely. Generally, MEC services rely on MEC devices and servers deployed at the edge of the network. Whether it is a MEC device or an edge server, most of them lack computing resources, and it is difficult to implement powerful security capabilities. Moreover, there are a large number of MEC service providers, different standards, and different protocols, which extend the attack interface of MEC services. In response to this situation, this paper proposes MECGuard, an attack detection solution designed for the MEC environment based on deep learning technology. Based on its distributed architecture designed for the MEC environment, MECGuard implements a lightweight TCP-level protocol extractor based on Decision Tree, and an attack detection network based on Gated Recurrent Unit (GRU). Experiments prove that MECGuard could have a good performance of malicious traffic detection in the EMC environment.
ISSN:0140-3664
1873-703X
DOI:10.1016/j.comcom.2021.02.022