Machine Learning based Intrusion Detection Strategy for SDON

ZHU Jia-hao; XU Kai; WANG Yan-hao; LU Yu-bin; XUAN Han; SHEN Jian-hua

doi:10.13756/j.gtxyj.2020.06.009

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Machine Learning based Intrusion Detection Strategy for SDON

- Machine Learning based Intrusion Detection Strategy for SDON
- Study on Optical communication Issue 6, Pages: 33-36(2020)
- 作者机构：
  
  南京邮电大学通信与信息工程学院
- 作者简介：
- 基金信息：
- DOI：10.13756/j.gtxyj.2020.06.009
  CLC： TN929.1;TP181
- Published：2020，
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[1]朱嘉豪,徐凯,王炎豪,陆煜斌,宣涵,沈建华.基于机器学习的软件定义光网络入侵检测策略[J].光通信研究,2020(06):33-36.

ZHU Jia-hao, XU Kai, WANG Yan-hao, et al. Machine Learning based Intrusion Detection Strategy for SDON. [J]. (6):33-36(2020)
[1]朱嘉豪,徐凯,王炎豪,陆煜斌,宣涵,沈建华.基于机器学习的软件定义光网络入侵检测策略[J].光通信研究,2020(06):33-36. DOI： 10.13756/j.gtxyj.2020.06.009.

ZHU Jia-hao, XU Kai, WANG Yan-hao, et al. Machine Learning based Intrusion Detection Strategy for SDON. [J]. (6):33-36(2020) DOI： 10.13756/j.gtxyj.2020.06.009.

摘要

软件定义光网络（SDON）中

控制平面可能遭遇入侵威胁从而对网络的稳定可靠服务供给造成影响。文章针对SDON集中控制平面安全问题提出了一种基于机器学习的入侵检测策略

采用孤立森林算法来检测点异常

采用指数权重移动平均（EWMA）算法来检测序列异常。理论分析和仿真实验结果表明

所提的基于机器学习的SDON检测技术能够实现90%点异常检测准确率和85%序列异常检测准确率。

Abstract

In the Software Defined Optical Network（SDON）

control plane may encounter various intrusion threat

which may affect the stable and reliable service provisioning in the network. Aiming at control plane security problems in SDON

machine learning based intrusion detection strategy is proposed

which utilizes Isolation Forest algorithm to detect point anomalies and Exponentially Weighted Moving Average（EWMA） algorithm to detect sequence anomalies. Theoretical analysis and simulation experimental results show that the proposed SDON detection technology based on machine learning can achieve 90% accuracy of point anomaly detection and 85% accuracy of sequence anomaly detection.

关键词

软件定义光网络控制平面异常检测机器学习

Keywords

SDONcontrol planeabnormal detectionmachine learning

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