Currently existing intrusion-detection systems can only meet the needs of the people for defense against the known threat, and lag in the detection of the unknown threat. To solve this problem, this study considers that the character of an unknown threat can evolve from known threats and propose a network unknown-threat detection algorithm intrusion detection method based on generating & evolution (IDM-GE) based on a generation countermeasure network and evolutionary computation. The intrusion detection method based on generating algorithm can balance the data set, make the classifier learn the characteristics of normal traffic and attack traffic more fairly, and increase the diversity of attack traffic distribution by dynamic games. The intrusion detection method based on evolution algorithm can mutate and evolve, and the combination of the generating algorithm and evolutionary algorithm can generalize the features of unknown threats from known threats in a large dynamic range while also improving the detection accuracy of unknown-threat traffic. The experimental results show that the proposed IDM-GE algorithm improves the detection accuracy and recall rate to more than 91% compared with the traditional ResNet algorithm.

中文翻译：

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基于生成对抗网络和进化算法的网络未知威胁检测

目前现有的入侵检测系统只能满足人们对已知威胁的防御需求，对未知威胁的检测滞后。为解决这一问题，本研究认为未知威胁的特征可以从已知威胁演化而来，提出一种基于生成对抗网络和进化进化的网络未知威胁检测算法（IDM-GE）入侵检测方法。计算。基于生成算法的入侵检测方法可以平衡数据集，使分类器更公平地学习正常流量和攻击流量的特征，通过动态博弈增加攻击流量分布的多样性。基于进化算法的入侵检测方法可以变异进化，生成算法与进化算法相结合，可以在较大的动态范围内从已知威胁中泛化未知威胁的特征，同时提高未知威胁流量的检测精度。实验结果表明，与传统的ResNet算法相比，所提出的IDM-GE算法将检测准确率和召回率提高到91%以上。