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Generation of Network Traffic Using WGAN-GP and a DFT Filter for Resolving Data Imbalance

机译:使用WGAN-GP和DFT过滤器生成网络流量,以解决数据不平衡问题

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The intrinsic features of Internet networks lead to imbalanced class distributions when datasets are conformed, phenomena called Class Imbalance and that is attaching an increasing attention in many research fields. In spite of performance losses due to Class Imbalance, this issue has not been thoroughly studied in Network Traffic Classification and some previous works are limited to few solutions and/or assumed misleading methodological approaches. In this study, we propose a method for generating network attack traffic to address data imbalance problems in training datasets. For this purpose, traffic data was analyzed based on deep packet inspection and features were extracted based on common traffic characteristics. Similar malicious traffic was generated for classes with low data counts using Wasserstein generative adversarial networks (WGAN) with a gradient penalty algorithm. The experiment demonstrated that the accuracy of each dataset was improved by approximately 5% and the false detection rate was reduced by approximately 8%. This study has demonstrated that enhanced learning and classification can be achieved by solving the problem of degraded performance caused by data imbalance in datasets used in deep learning based intrusion detection systems.
机译:当符合数据集时,Internet网络的内在特征会导致类分布不平衡,这种现象称为类不平衡,这在许多研究领域中都日益引起人们的关注。尽管由于类不平衡而导致性能损失,但尚未在网络流量分类中对此问题进行彻底研究,并且某些先前的工作仅限于很少的解决方案和/或假定的误导方法。在这项研究中,我们提出了一种生成网络攻击流量的方法,以解决训练数据集中的数据不平衡问题。为此,基于深度包检查对交通数据进行了分析,并根据常见交通特征提取了特征。使用具有梯度罚分算法的Wasserstein生成对抗网络(WGAN),针对数据量少的类生成了类似的恶意流量。实验表明,每个数据集的准确性提高了约5%,错误检测率降低了约8%。这项研究表明,通过解决基于深度学习的入侵检测系统中使用的数据集中的数据不平衡导致的性能下降问题,可以实现增强的学习和分类。

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