NETWORK ABNORMAL TRAFFIC DETECTION FRAMEWORK BASED ON DEEP REINFORCEMENT LEARNING

doi:10.1109/MWC.011.2200320

NETWORK ABNORMAL TRAFFIC DETECTION FRAMEWORK BASED ON DEEP REINFORCEMENT LEARNING

2024-06-01

1

PRE

AI

DOI：10.1109/MWC.011.2200320

Original

Original request for help

Save

Abstract

En 中文

Because wireless communication network attacks continue to evolve, cyber security is becoming more and more important, requiring network security solutions to be constantly updated. In addition, wireless communication network traffic in high-speed networks is massive, high-dimensional, dynamic, and so on, making attacks difficult to detect in real-time. Unknown attacks are also difficult to identify. This article proposes a framework for abnormal traffic detection based on deep reinforcement learning, mainly consisting of four stages: data collection, dataset pre-processing, feature selection, and model detection. Experimental results show that the abnormal traffic detection framework has improved detection performance.

Keywords:

Feature extraction

Telecommunication traffic

Deep reinforcement learning

Optimization

Machine learning

Wireless communication

Network security

NETWORK ABNORMAL TRAFFIC DETECTION FRAMEWORK BASED ON DEEP REINFORCEMENT LEARNING

Abstract

Journal

Organization