基于注意力机制的社交垃圾文本检测方法

曲强; 于洪涛; 黄瑞阳

doi:10.11959/j.issn.2096-109x.2020002

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基于注意力机制的社交垃圾文本检测方法

学术论文 | 更新时间：2024-06-03

- 基于注意力机制的社交垃圾文本检测方法
- Attention-based approach of detecting spam in social networks
- 网络与信息安全学报 2020年6卷第1期页码：54-61
- 作者机构：
- 作者简介：
  
  [ "曲强（1994- ），男，黑龙江齐齐哈尔人，国家数字交换系统工程技术研究中心硕士生，主要研究方向为网络空间安全、大数据分析与处理、复杂网络异常用户检测" ]
  [ "于洪涛（1970- ），男，辽宁丹东人，博士，国家数字交换系统工程技术研究中心研究员，主要研究方向为网络大数据分析与处理" ]
  [ "黄瑞阳（1986- ），男，福建漳州人，博士，国家数字交换系统工程技术研究中心助理研究员，主要研究方向为文本挖掘、图挖掘" ]
- 基金信息：
  
  国家自然科学基金创新研究群体基金资助项目;The Foundation for Innovative Research Groups of the National Natural Science Foundation of China(61521003)
- DOI：10.11959/j.issn.2096-109x.2020002
  中图分类号： TP309
- 网络首发：2020-02，
  
  纸质出版：2020-02-15
- 稿件说明：
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曲强, 于洪涛, 黄瑞阳. 基于注意力机制的社交垃圾文本检测方法[J]. 网络与信息安全学报, 2020,6(1):54-61.

Qiang QU, Hongtao YU, Ruiyang HUANG. Attention-based approach of detecting spam in social networks[J]. Chinese Journal of Network and Information Security, 2020, 6(1): 54-61.
曲强, 于洪涛, 黄瑞阳. 基于注意力机制的社交垃圾文本检测方法[J]. 网络与信息安全学报, 2020,6(1):54-61. DOI： 10.11959/j.issn.2096-109x.2020002.

Qiang QU, Hongtao YU, Ruiyang HUANG. Attention-based approach of detecting spam in social networks[J]. Chinese Journal of Network and Information Security, 2020, 6(1): 54-61. DOI： 10.11959/j.issn.2096-109x.2020002.

摘要

在社交网络中，大量的垃圾文本严重威胁用户的信息安全与社交网站的信用体系。针对噪声性与稀疏性问题，提出一种基于注意力机制的卷积神经网络检测方法。在经典卷积神经网络的基础上，该方法增加了过滤层，并在过滤层设计基于朴素贝叶斯权重技术的注意力机制，解决了噪声性问题。并且，它改变了池化层原有的策略，采用基于注意力机制的池化策略，缓解了稀疏性问题。结果表明，相对于其他检测方法，所提方法的检测准确率在4个数据集上分别提高了1.32%、2.15%、0.07%、1.63%。

Abstract

In social networks

a large amount of spam has seriously threaten users' information security and the credit system of social websites.Aiming at the noise and sparsity problems

an attention-based CNN method was proposed to detect spam.On the basis of classical CNN

this method added a filter layer in which an attention mechanism based on Naive Bayesian weighting technology was designed to solve the noise issue.What’s more

instead of the original pooling strategy

it adapted an attention-based pooling policy to alleviate the sparsity problem.Compared with other methods

the results show that the accuracy has increased by 1.32%，2.15%，0.07%，1.63% on four different data sets.

关键词

Keywords

references

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