首页|SDN环境下基于CNN-BiLSTM的入侵检测研究

SDN环境下基于CNN-BiLSTM的入侵检测研究

扫码查看
软件定义网络(SDN)是一种将控制层和数据层分离的新型网络架构,在实现网络集中管理和可编程性的同时也面临易受到入侵攻击的问题.针对此问题设计了检测防御机制.利用深度学习算法,对数据集进行处理后,融合卷积神经网络(CNN)和双向长短期记忆网络(BiL-STM),设计了 CNN-BiLSTM模型检测攻击,利用SDN可编程性设计了防御机制,搭建基于SDN的网络平台进行仿真实验.实验结果表明,所设计方法相较传统检测方法可更准确检测出入侵流量,并在检测出后有效实现了防御功能.
Research on Intrusion Detection Based on CNN-BiLSTM in SDN Environment
Software Defined Network(SDN)is a new network architecture that separates the con-trol layer from the data layer.While realizing centralized management and programmability of the net-work,it also faces the problem of vulnerability to intrusion attacks.A detection and defense mechanism is designed for this problem.After using the deep learning algorithm to process the data set,the CNN-BiLSTM model is designed to detect attacks by integrating the convolutional neural network(CNN)and the bidirectional long-term and short-term memory network(BiLSTM),and the defense mechanism is designed by using SDN programmability.A network platform based on SDN is built for simulation experiments.Experimental results show that the designed method can detect intrusion traffic more accu-rately than traditional detection methods and can effectively implement defense functions after detection.

software defined networkdeep learningconvolutional neural networkbidirectional long short-term memory networkintrusion detectionance

韩炎龙、翟亚红

展开 >

湖北汽车工业学院电气与信息工程学院,湖北十堰 442002

软件定义网络 深度学习 卷积神经网络 双向长短期记忆网络 入侵检测

湖北省教育厅科研计划重点项目湖北省科技厅重点研发计划项目

D202118022022BEC008

2024

佳木斯大学学报(自然科学版)
佳木斯大学

佳木斯大学学报(自然科学版)

影响因子:0.159
ISSN:1008-1402
年,卷(期):2024.42(3)
  • 13