基于深度学习的数字化通信网络异常检测方法研究  

Research on the Anomaly Detection Method of Digital Communication Network Based on Deep Learning

作　　者：宁静雁 NING Jingyan(Datong Normal College,Datong 037000,China)

机构地区：[1]大同师范高等专科学校,山西大同037000

出　　处：《通信电源技术》2025年第4期180-182,共3页Telecom Power Technology

摘　　要：随着数字化通信网络的普及和复杂性的提升,网络异常检测成为确保通信稳定性和安全性的关键任务。深度学习是人工智能领域的一大突破,在处理大量数据和识别复杂模式方面具有显著优势。全面研究基于深度学习的数字化通信网络异常检测方法,包括神经网络架构的选择、异常特征的提取技术、时序异常检测以及实时检测系统的实施。With the popularization and increasing complexity of digital communication networks,network anomaly detection has become a key task to ensure communication stability and security.Deep learning is a major breakthrough in the field of artificial intelligence,with significant advantages in processing large amounts of data and identifying complex patterns.A comprehensive study on anomaly detection methods for digital communication networks based on deep learning,including the selection of neural network architecture,extraction techniques for anomaly features,temporal anomaly detection,and implementation of real-time detection systems.

关 键 词：深度学习 数字化 通信网络 异常检测 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]