Attacks Detection Approach Based on a Reinforcement Learning Process to Secure 5G Wireless Network

Fifth Generation (5G) wireless network will be a subject to a variety of cyber-threats from advanced and complex attacks. In this article, we aim to secure the 5G wireless systems from the most dangerous and advanced network attacks, e.g., jamming and Distributed Denial of Service (DDoS) attacks. We...

Full description

Saved in:
Bibliographic Details
Published inIEEE/CIC International Conference on Communications in China - Workshops (Online) pp. 1 - 6
Main Author Sedjelmaci, Hichem
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2020
Subjects
5G mobile communication
5G wireless network
Computer architecture
Computer crime
Jamming
Machine learning algorithms
Monitoring
Network attacks detection
Reinforcement learning
Wireless networks
Online AccessGet full text

Cover

More Information
Summary:Fifth Generation (5G) wireless network will be a subject to a variety of cyber-threats from advanced and complex attacks. In this article, we aim to secure the 5G wireless systems from the most dangerous and advanced network attacks, e.g., jamming and Distributed Denial of Service (DDoS) attacks. We propose and develop a new cooperative attack detection based on a hierarchical Reinforcement Learning (RL) process to identify the network attacks. The cooperative detection is performed with a distributed detection systems executed at the different critical 5G network's organs such as access point, base station and servers. According to our experiments results, the proposed RL detection system enhances a detection of new misbehaviors attacks.
ISSN:2474-9133
DOI:10.1109/ICCWorkshops49005.2020.9145438