The performance of classifiers has a direct impact on the effectiveness of intrusion detection system. Thus, most researchers aim to improve the detection performance of classifiers. However, classifiers can only get limited useful information from the limited number of labeled training samples, which usually affects the generalization of classifiers. In order to enhance the network intrusion detection classifiers, we resort to adversarial training, and a novel supervised learning framework using generative adversarial network for improving the performance of the classifier is proposed in this paper. The generative model in our framework is utilized to continuously generate other complementary labeled samples for adversarial training and assist the classifier for classification, while the classifier in our framework is used to identify different categories. Meanwhile, the loss function is deduced again, and several empirical training strategies are proposed to improve the stabilization of the supervised learning framework. Experimental results prove that the classifier via adversarial training improves the performance indicators of intrusion detection. The proposed framework provides a feasible method to enhance the performance and generalization of the classifier.

中文翻译：

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使用监督对抗训练增强网络入侵检测分类器

分类器的性能直接影响入侵检测系统的有效性。因此，大多数研究人员的目标是提高分类器的检测性能。然而，分类器只能从有限数量的标记训练样本中获得有限的有用信息，这通常会影响分类器的泛化能力。为了增强网络入侵检测分类器，我们采用对抗性训练，本文提出了一种使用生成对抗网络来提高分类器性能的新型监督学习框架。我们框架中的生成模型用于不断生成其他互补标记样本进行对抗训练，并协助分类器进行分类，而我们框架中的分类器用于识别不同的类别。同时，再次推导损失函数，并提出了几种经验训练策略来提高监督学习框架的稳定性。实验结果证明，该分类器通过对抗训练提高了入侵检测的性能指标。所提出的框架提供了一种可行的方法来提高分类器的性能和泛化能力。