Network Intrusion Detection Model Based on Ensemble of Denoising Adversarial Autoencoder
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NETL
NSTL
万方数据
网络安全问题给数据的完整性和设备服务的可靠性带来了许多不易察觉的威胁,因此提出一种具有高可靠性的网络入侵检测模型对网络安全具有重要的研究意义.由于单一自编码器入侵检测模型训练时对无效特征泛化性较强,检测有效结果较困难.本研究提出了一种基于集成降噪对抗自编码器(Ensemble of Denoising Adversrial Autoencoder,EDAAE)的网络入侵检测模型,该模型相较于传统异常检测模型准确率更高,可靠性更强.利用对抗自编码器的对抗性学习思想(Adversrial Autoencoder,AAE),在原始模型中加入判别器模块,将编码器部分作为生成器.使编码器生成的数据隐空间分布与原始数据分布相匹配,也降低了模型对无效特征的泛化性来提高检测准确率.同时引入降噪自编码器和集成操作,防止对抗学习过程中出现过拟合情况.在CICIDS2018流量数据集上的实验表明,所提出的异常检测模型的准确率达到了95.23％,与传统的自编码器和其他现有的异常检测模型相比,在整体性能上优于其他方法.
Network security problems bring many imperceptible threats to the integrity of data and the reliability of device services,so proposing a network intrusion detection model with high reliability is of great research significance for network security.Due to the strong generalization of invalid features during training process,it is more difficult for single autoencoder intrusion detection model to obtain effective results.A network intrusion detection model based on the Ensemble of Denoising Adversarial Autoencoder(EDAAE)was proposed,which had higher accuracy and reliability compared to the traditional anomaly detection model.Using the adversarial learning idea of Adversarial Autoencoder(AAE),the discriminator module was added to the original model,and the encoder part was used as the generator.The distribution of the hidden space of the data generated by the encoder matched with the distribution of the original data.The generalization of the model to the invalid features was also reduced to improve the detection accuracy.At the same time,the denoising autoencoder and integrated operation was introduced to prevent overfitting in the adversarial learning process.Experiments on the CICIDS2018 traffic dataset showed that the proposed intrusion detection model achieves an Accuracy of 95.23%,which out performs traditional self-encoders and other existing intrusion detection models methods in terms of overall performance.
NETL
NSTL
万方数据
