基于无向图流程创建聚合推理的近邻SDN可疑流量检测

徐镇; 张小平; 张海翔

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基于无向图流程创建聚合推理的近邻SDN可疑流量检测

更新时间：2026-09-04

- 基于无向图流程创建聚合推理的近邻SDN可疑流量检测
- Undirected Graph Information Aggregation Reasoning Based SDN Suspicious Traffic Detection
- 微电子学与计算机 2017年34卷第12期页码：5-10
- 作者机构：
  
  清华大学计算机科学与技术系,北京,100084
- 作者简介：
- 基金信息：
  
  清华大学青年教师资助基金 20160525
- DOI：
  中图分类号：
- 纸质出版：2017
- 稿件说明：
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徐镇, 张小平, 张海翔. 基于无向图流程创建聚合推理的近邻SDN可疑流量检测[J]. 微电子学与计算机, 2017, 34(12): 5-10, 15.

XU Zhen, ZHANG Xiao-ping, ZHANG Hai-xiang. Undirected Graph Information Aggregation Reasoning Based SDN Suspicious Traffic Detection[J]. 2017, 34(12): 5-10.
徐镇, 张小平, 张海翔. 基于无向图流程创建聚合推理的近邻SDN可疑流量检测[J]. 微电子学与计算机, 2017, 34(12): 5-10, 15. DOI：

XU Zhen, ZHANG Xiao-ping, ZHANG Hai-xiang. Undirected Graph Information Aggregation Reasoning Based SDN Suspicious Traffic Detection[J]. 2017, 34(12): 5-10. DOI：

摘要

为提高软件定义网络(Software Defined Network

SDN)可疑流量检测算法性能

提出一种基于无向图流程创建信息聚合推理的近邻SDN可疑流量检测方法.利用OpenFlow模块进行数据流创建

并进行入侵规则构建.然后

基于马尔可夫链的图节点/边表示

采用无向图方式进行攻击特征表示

实现新增攻击的增量化表示

降低无向图构建的计算复杂度

并利用近邻算法对无向图恶意攻击流量特征进行分类

实现攻击有效检测.最后

通过构建的SDN测试平台

对所提算法的性能进行验证.

Abstract

In order to improve the performance of suspicious traffic detection algorithm in software defined network

this paper proposes a method for detecting suspicious traffic of nearest neighbor based on undirected graph process. OpenFlow module is used to create data stream

and the intrusion rules are constructed. Then

based on the map nodes/edges represent with the Markov chain

the undirected graph was used to represent the attack characteristics

it realized the incremental implementation of new attacks

which could reduce the computational complexity of constructing undirected graph

and the nearest neighbor algorithm was used to classify the malicious attack traffic characteristics of undirected graph to achieve effective detection of attacks. Finally

the performance of the proposed algorithm is verified by the SDN test platform.

关键词

Keywords

references

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汪伦杰, 廖兴宇, 潘伟杰. 基于信号均值滤波+k-means+WKNN的Wifi指纹定位算法研究[J]. 微电子学与计算机, 2017, 34(3): 30-34. DOI: 30

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