Network Intrusion Detection Model Based on Ensemble of Denoising Adversarial Autoencoder

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.

考虑不平稳信号的振动传感器稳定性控制技术

外界因素干扰造成振动传感器产生异常振动,导致信号传递不平稳,影响传感器使用效果。为了提升振动传感器稳定性,提出考虑不平稳信号的振动传感器稳定性控制技术。根据振动信号的幅度谱均值计算标准差,通过相位计算判定振动信号平稳成分,分解振动传感器信号,采用动态小波阈值的方法,对振动传感器不平稳信号进行降噪。构建振动传感器运动模型,获取振动传感器频率,采用传递函数对振动传感器传递信号进行校正,以此实现振动传感器稳定性控制。测试结果表明,所提方法的振动频率信号幅值波动较小,处于27 dB~33 dB之间;Z轴向校正振动传感器灵敏度影响效果较高,振动频率为2500 Hz时可以达到0.20 nm/gn;归一化频谱较为稳定,处于27 dB~33 dB之间。由此可知,所提方法具有较强的稳定性。