基于非对称卷积自编码器和支持向量机的入侵检测模型

王佳林; 刘吉强; 赵迪; 王盈地; 相迎宵; 陈彤; 童恩栋; 牛温佳

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

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基于非对称卷积自编码器和支持向量机的入侵检测模型

论文 | 更新时间：2024-06-03

- 基于非对称卷积自编码器和支持向量机的入侵检测模型
- Intrusion detection model based on non-symmetric convolution auto-encode and support vector machine
- 网络与信息安全学报 2018年4卷第11期页码：57-68
- 作者机构：
- 作者简介：
  
  [ "王佳林（1994-），女，山西太原人，北京交通大学硕士生，主要研究方向为信息安全和人工智能安全。" ]
  [ "刘吉强（1973-），男，山东海阳人，博士，北京交通大学教授、博士生导师，主要研究方向为隐私保护、可信计算、安全协议设计与分析。" ]
  [ "赵迪（1995-），女，河北承德人，北京交通大学硕士生，主要研究方向为信息安全和人工智能安全。" ]
  [ "王盈地（1995-），女，河北石家庄人，北京交通大学硕士生，主要研究方向为信息安全。" ]
  [ "相迎宵（1994-），女，山西运城人，北京交通大学硕士生，主要研究方向为信息安全和人工智能安全。" ]
  [ "陈彤（1993-），女，天津人，北京交通大学博士生，主要研究方向为信息安全和人工智能安全。" ]
  [ "童恩栋（1986-），男，山东聊城人，博士，北京交通大学讲师，主要研究方向为智能信息处理、网络空间安全。" ]
  [ "牛温佳（1982-），男，宁夏银川人，博士，北京交通大学教授、博士生导师，主要研究方向为人工智能安全。" ]
- 基金信息：
  
  中央高校基础研究基金资助项目;The Fundamental Research Funds for the Central Universities(2017RC016;2018JBZ103);国家自然科学基金资助项目;The National Natural Science Foundation of China(61672092);信息保障科技实验室基金资助项目;Science and Technology on Information Assurance Laboratory(614200103011711);北京优秀人才培养基金资助项目;Beijing Excellent Talent Training Project(BMK2017B02-2);国家留学基金委资助项目;China Scholarship Council(201807095014)
- DOI：10.11959/j.issn.2096-109x.2018086
  中图分类号： TP18
- 网络首发：2018-11，
  
  纸质出版：2018-11-15
- 稿件说明：
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王佳林, 刘吉强, 赵迪, 等. 基于非对称卷积自编码器和支持向量机的入侵检测模型[J]. 网络与信息安全学报, 2018,4(11):57-68.

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.
王佳林, 刘吉强, 赵迪, 等. 基于非对称卷积自编码器和支持向量机的入侵检测模型[J]. 网络与信息安全学报, 2018,4(11):57-68. DOI： 10.11959/j.issn.2096-109x.2018086.

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.

关键词

Keywords

references

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