Metric-based learning approach to botnet detection with small samples

Honggang LIN; Junjing ZHU; Lin CHEN

doi:10.11959/j.issn.2096-109x.2023076

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Metric-based learning approach to botnet detection with small samples

Papers | 更新时间：2024-06-03

- Metric-based learning approach to botnet detection with small samples
- Chinese Journal of Network and Information Security Vol. 9, Issue 5, Pages: 33-47(2023)
- 作者机构：
  
  1. 成都信息工程大学网络空间安全学院，四川成都 610225
  2. 先进密码技术与系统安全四川省重点实验室，四川成都 610225
  3. 网络空间安全态势感知与评估安徽省重点实验室，安徽合肥 230037
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-109x.2023076
  CLC： TP309.5
- Online First：2023-10，
  
  Published：25 October 2023
- 稿件说明：
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Honggang LIN, Junjing ZHU, Lin CHEN. Metric-based learning approach to botnet detection with small samples[J]. Chinese Journal of Network and Information Security, 2023, 9(5): 33-47.
DOI：

Honggang LIN, Junjing ZHU, Lin CHEN. Metric-based learning approach to botnet detection with small samples[J]. Chinese Journal of Network and Information Security, 2023, 9(5): 33-47. DOI： 10.11959/j.issn.2096-109x.2023076.

摘要

僵尸网络给互联网带来了极大的威胁，尽早有效地检测出僵尸网络对于维护网络空间安全具有重要的实践意义。然而在僵尸网络发现初期，可获得带标记样本数量较少，这使得目前大部分基于深度学习的检测方法无法得到充分训练，导致检测结果不佳。因此，提出了一种用于小样本下的基于度量学习的僵尸网络检测方法 BT-RN，利用基于任务的元学习训练策略优化模型，在任务中引入验证集并通过度量验证样本和训练样本特征表示之间的相似度来快速累积经验，从而降低模型对标记样本空间的依赖；引入了特征级注意力机制，通过计算特征中各维度的注意力系数，重新整合特征表示并分配重要度关注来优化特征表示，从而减少深度神经网络在小样本中的特征稀疏问题；引入了残差网络设计模式，利用跳跃链接来规避增加特征级注意力机制模块后，较深的网络所带来的模型退化和梯度消失风险。实验结果表明，在小样本背景下，所提僵尸网络检测方法的正确率和模型泛化能力优于其他小样本检测方法和深度学习检测方法。

Abstract

Botnets pose a great threat to the Internet

and early detection is crucial for maintaining cybersecurity.However

in the early stages of botnet discovery

obtaining a small number of labeled samples restricts the training of current detection models based on deep learning

leading to poor detection results.To address this issue

a botnet detection method called BT-RN

based on metric learning

was proposed for small sample backgrounds.The task-based meta-learning training strategy was used to optimize the model.The verification set was introduced into the task and the similarity between the verification sample and the training sample feature representation was measured to quickly accumulate experience

thereby reducing the model’s dependence on the labeled sample space.The feature-level attention mechanism was introduced.By calculating the attention coefficients of each dimension in the feature

the feature representation was re-integrated and the importance attention was assigned to optimize the feature representation

thereby reducing the feature sparseness of the deep neural network in small samples.The residual network design pattern was introduced

and the skip link was used to avoid the risk of model degradation and gradient disappearance caused by the deeper network after increasing the feature-level attention mechanism module.

关键词

Keywords

references

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