基于生成对抗网络的入侵检测类别不平衡问题数据增强方法
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TP391

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A Data Augmentation Method for Intrusion Detection Imbalance Problem using Generative Adversarial Networks
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    摘要:

    数据类别不平衡问题是制约机器学习技术在入侵检测领域应用效果的重要因素。当训练数据不均衡时,训练得到模型的分类结果往往倾向多数类,从而极大影响分类效果。针对基于机器学习算法进行入侵检测时训练样本不均衡以及由于数据隐私性导致训练样本不足和更新慢的问题,提出一种基于生成对抗网络和深度神经网络相结合的入侵数据增强方法,以实现样本集的类别均衡。通过NSL-KDD数据集对模型评估,本文所提方法不仅具有较高的准确率,而且对未知攻击和只有少数样本的攻击类型具有较高的检测率。

    Abstract:

    Class imbalance problem is a key factor that restricts the application of machine learning technology in the field of intrusion detection. The classification result will be significantly affected if the training data is imbalanced due to the classifier’s tendency towards the majority class. A data augmentation method for intrusion detection combining generative adversarial networks and deep neural network is proposed in this paper in order to solve the problem of sample imbalance and lack of sample because of the confidentiality of the data. Finally, the model is evaluated on the NSL-KDD dataset and the result shows that our method not only has higher accuracy rate, but also has higher detection rate for unknown attacks and attack types with only a few samples compared with the traditional algorithms.

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孙佳佳,李承礼,常德显,等. 基于生成对抗网络的入侵检测类别不平衡问题数据增强方法[J]. 科学技术与工程, 2022, 22(18): 7965-7971.
Sun Jiajia, Li Chengli, Chang Dexian, et al. A Data Augmentation Method for Intrusion Detection Imbalance Problem using Generative Adversarial Networks[J]. Science Technology and Engineering,2022,22(18):7965-7971.

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历史
  • 收稿日期:2021-08-10
  • 最后修改日期:2022-03-29
  • 录用日期:2022-01-15
  • 在线发布日期: 2022-07-14
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