舰船电子工程2024，Vol.44Issue(10)：85-89,5.DOI:10.3969/j.issn.1672-9730.2024.10.017
基于深度学习的SDN环境下异常流量检测方法
Abnormal Traffic Detection Method in SDN Based on Deep Learning
张瑞1
作者信息
- 1. 中电科网络安全科技股份有限公司 北京 100041
- 折叠
摘要
Abstract
Aiming at the problems that traditional anomaly detection methods are complicated in algorithm,high in calcula-tion cost and generate extra traffic when deployed in SDN network,a lightweight anomaly traffic detection method based on deep learning is proposed.By analyzing the importance of traffic data features,detection data is constructed,correlation information of detection data is extracted by using circular neural network,and anomaly traffic is detected by using lightweight classification func-tion.The experimental results show that the proposed method has obvious advantages over the traditional detection methods in terms of accuracy,recall and detection time,and has the characteristics of simple deployment and little impact on the performance of SDN controller.关键词
深度学习/软件定义网络/异常检测/异常缓解Key words
deep learning/software-defined network/abnormal detection/abnormal relief分类
信息技术与安全科学引用本文复制引用
张瑞..基于深度学习的SDN环境下异常流量检测方法[J].舰船电子工程,2024,44(10):85-89,5.
