基于DCNN-GRU架构的SDN异常流量检测方法

IF 5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Xueyuan Duan, Kun Wang, Yu Fu, Taotao Liu, Yihan Yu, Jianqiao Xu, Lu Wang
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引用次数: 0

摘要

针对软件定义网络(SDN)中集中式单架构异常流量检测方法消耗大量计算和网络资源,并可能导致SDN网络服务质量下降的问题,本文提出了一种基于分布式卷积神经网络和门递归单元(DCNN-GRU)架构的SDN网络大规模异常流量检测方法。该方法利用部署在每个控制器上的基于CNN的轻量级检测代理,初步提取流量特征。然后将特征数据输入到托管在云端的基于gru的深度检测模型中进行协同训练,完成最终的异常检测任务。由于特征提取任务分布在多个控制器上,因此云服务器只需要对提取的特征数据进行重新学习和分类,这比直接从原始流量数据中提取特征信息成本低,比传输完整数据包占用的带宽资源少。实验表明,该方法的异常检测准确率为0.9939,召回率为0.9831,虚警率仅为0.0244,与传统检测方法相比,具有更高的检测精度和更低的虚警率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Abnormal Traffic Detection Method Based on DCNN-GRU Architecture in SDN

Abnormal Traffic Detection Method Based on DCNN-GRU Architecture in SDN

In response to the centralized single-architecture abnormal traffic detection method in Software Defined Network (SDN), which consumes massive computational and network resources, and may lead to the decline of service quality of SDN network, this paper proposes a large-scale abnormal traffic detection method of SDN network based on Distributed Convolutional Neural Networks and Gate Recurrent Unit (DCNN-GRU) architecture. This method utilizes lightweight detection agents based on CNN deployed on each controller to extract traffic features preliminarily. Then it inputs the feature data into the GRU-based deep detection model hosted in the cloud for collaborative training and completes the final abnormal detection task. Since the feature extraction tasks are distributed across multiple controllers, the cloud server only needs to relearn and classify the extracted feature data, which is less costly than directly extracting feature information from the original traffic data and occupies less bandwidth resources than transmitting complete data packets. The experiment shows that the method achieves an abnormal detection accuracy of 0.9939, a recall rate of 0.9831, and a false alarm rate of only 0.0244, obtaining a higher precision and lower false alarm rate than traditional detection methods.

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来源期刊
International Journal of Intelligent Systems
International Journal of Intelligent Systems 工程技术-计算机:人工智能
CiteScore
11.30
自引率
14.30%
发文量
304
审稿时长
9 months
期刊介绍: The International Journal of Intelligent Systems serves as a forum for individuals interested in tapping into the vast theories based on intelligent systems construction. With its peer-reviewed format, the journal explores several fascinating editorials written by today''s experts in the field. Because new developments are being introduced each day, there''s much to be learned — examination, analysis creation, information retrieval, man–computer interactions, and more. The International Journal of Intelligent Systems uses charts and illustrations to demonstrate these ground-breaking issues, and encourages readers to share their thoughts and experiences.
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