Anomaly intrusion detection based on modified SVM

Hui ZHANG; Cheng LIU

doi:10.11959/j.issn.2096-109x.2016.00092

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Anomaly intrusion detection based on modified SVM

Academic paper | 更新时间：2024-06-03

- Anomaly intrusion detection based on modified SVM
- Journal of network and information security Vol. 2, Issue 8, Pages: 68-73(2016)
- 作者机构：
  
  1. 新疆公安厅特别侦察队，新疆乌鲁木齐 830000
  2. 国家计算机网络应急技术处理协调中心，北京 100029
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-109x.2016.00092
  CLC： TP309/TP274
- Online First：2016-08，
  
  Published：15 August 2016
- 稿件说明：
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Hui ZHANG, Cheng LIU. Anomaly intrusion detection based on modified SVM[J]. Journal of network and information security, 2016, 2(8): 68-73.
DOI：

Hui ZHANG, Cheng LIU. Anomaly intrusion detection based on modified SVM[J]. Journal of network and information security, 2016, 2(8): 68-73. DOI： 10.11959/j.issn.2096-109x.2016.00092.

摘要

利用非参数检验的方法提取出对分类结果影响显著的特征变量，提出一种改进的 SVM多分类方法（D-SVM），其融合了判别分析，可以解决样本不均衡导致的分类不准确和误报率高的问题。将多分类问题处理成一个个二分类问题，D-SVM既可以保持SVM较好的分类准确性，同时又可以不受样本不均衡的影响，具有较低的误报率。将 D-SVM 应用到 KDD99数据集，结果表明，该方法具有较高的分类准确性和较低的误报率。

Abstract

A modified SVM multi-classification algorithm integrated with discriminant analysis (D-SVM) was pro-posed

which could solve the problem of low detection accuracy and high false alarm rate caused by unbalanced datasets.For a multi-classification problem could be divided into several binary classification problems

D-SVM could not only have the virtue of high detection accuracy

but also have a low false alarm rate even confronted with unbalanced datasets.Experiments based on KDD99 dataset verify the feasibility and validity of the integrated ap-proach.Results show that when confronted with multi-classification problems

D-SVM could achieve a high detec-tion accuracy and low false alarm rate even when SVM alone fails because of the unbalanced datasets.

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references

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