可变融合的随机注意力胶囊网络入侵检测模型

张兴兰; 尹晟霖

doi:10.11959/j.issn.1000-436x.2020220

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可变融合的随机注意力胶囊网络入侵检测模型

学术论文 | 更新时间：2024-06-05

- 可变融合的随机注意力胶囊网络入侵检测模型
- Intrusion detection model of random attention capsule network based on variable fusion
- 通信学报 2020年41卷第11期页码：160-168
- 作者机构：
  
  北京工业大学信息学部，北京 100022
- 作者简介：
  
  [ "张兴兰（1970- ），女，山西吕梁人，博士，北京工业大学教授，主要研究方向为密码学、信息安全等" ]
  [ "尹晟霖（1996- ），男，山东淄博人，北京工业大学硕士生，主要研究方向为深度学习、信息安全等" ]
- 基金信息：
  
  国家自然科学基金资助项目(61801008)
- DOI：10.11959/j.issn.1000-436x.2020220
  中图分类号： TP309
- 网络出版日期：2020-11，
  
  纸质出版日期：2020-11-25
- 稿件说明：
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张兴兰, 尹晟霖. 可变融合的随机注意力胶囊网络入侵检测模型[J]. 通信学报, 2020,41(11):160-168.

Xinglan ZHANG, Shenglin YIN. Intrusion detection model of random attention capsule network based on variable fusion[J]. Journal on communications, 2020, 41(11): 160-168.
张兴兰, 尹晟霖. 可变融合的随机注意力胶囊网络入侵检测模型[J]. 通信学报, 2020,41(11):160-168. DOI： 10.11959/j.issn.1000-436x.2020220.

Xinglan ZHANG, Shenglin YIN. Intrusion detection model of random attention capsule network based on variable fusion[J]. Journal on communications, 2020, 41(11): 160-168. DOI： 10.11959/j.issn.1000-436x.2020220.

摘要

为了增强检测模型的准确率与泛化性，提出了一种可变融合的随机注意力胶囊网络的入侵检测模型，通过特征动态融合，模型能够更好地捕捉数据特征；同时使用随机注意力机制，减少了对训练数据的依赖，使模型更具有泛化能力。所提模型在NSL-KDD和UNSW-NB15数据集上进行验证，实验表明，模型在2种测试集上的准确率分别达到了99.49%和98.60%。

Abstract

In order to enhance the accuracy and generalization of the detection model

an intrusion detection model of random attention capsule network with variable fusion was proposed.Through dynamic feature fusion

the model could better capture data features.At the same time

random attention mechanism was used to reduce the dependence on training data and make the model more generalization.The model was validated on NSL-KDD and UNSW-NB15 datasets.The experimental results show that the accuracy of the model on the two test sets is 99.49% and 98.60% respectively.

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references

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