Intrusion detection model based on non-symmetric convolution auto-encode and support vector machine

Jialin WANG; Jiqiang LIU; Di ZHAO; Yingdi WANG; Yingxiao XIANG; Tong CHEN; Endong TONG; Wenjia NIU

doi:10.11959/j.issn.2096-109x.2018086

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Intrusion detection model based on non-symmetric convolution auto-encode and support vector machine

Papers | 更新时间：2024-06-03

- Intrusion detection model based on non-symmetric convolution auto-encode and support vector machine
- Chinese Journal of Network and Information Security Vol. 4, Issue 11, Pages: 57-68(2018)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-109x.2018086
  CLC： TP18
- Online First：2018-11，
  
  Published：15 November 2018
- 稿件说明：
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Jialin WANG, Jiqiang LIU, Di ZHAO, et al. Intrusion detection model based on non-symmetric convolution auto-encode and support vector machine[J]. Chinese Journal of Network and Information Security, 2018, 4(11): 57-68.
DOI：

Jialin WANG, Jiqiang LIU, Di ZHAO, et al. Intrusion detection model based on non-symmetric convolution auto-encode and support vector machine[J]. Chinese Journal of Network and Information Security, 2018, 4(11): 57-68. DOI： 10.11959/j.issn.2096-109x.2018086.

摘要

网络入侵检测系统在防护网络安全中占据重要地位，随着科技不断发展，目前的入侵技术没有考虑到检测技术的可扩展性、可持续性以及训练时间长短，无法应对现代复杂多变的网络异常流量。针对这些问题，提出了一种新的深度学习方法，使用无监督的非对称卷积自编码器，对数据进行特征学习。另外，提出了一种新的基于非对称卷积自编码器和多类支持向量机相结合的方法。在 KDD99 数据集上进行了实验，实验结果表明，该方法取得了良好的结果，与其他方法相比显著减少了训练时间，进一步提高了网络入侵检测技术。

Abstract

Network intrusion detection system plays an important role in protecting network security.With the continuous development of science and technology

the current intrusion technology cannot cope with the modern complex and volatile network abnormal traffic

without taking into account the scalability

sustainability and training time of the detection technology.Aiming at these problems

a new deep learning method was proposed

which used unsupervised non-symmetric convolutional auto-encoder to learn the characteristics of the data.In addition

a new method based on the combination of non-symmetric convolutional auto-encoder and multi-class support vector machine was proposed.Experiments on the data set of KDD99 show that the method achieves good results

significantly reduces training time compared with other methods

and further improves the network intrusion detection technology.

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Keywords

references

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