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 si...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.展开更多
Aiming at the problem of small area human occlusion in gait recognition,a method based on generating adversarial image inpainting network was proposed which can generate a context consistent image for gait occlusion a...Aiming at the problem of small area human occlusion in gait recognition,a method based on generating adversarial image inpainting network was proposed which can generate a context consistent image for gait occlusion area.In order to reduce the effect of noise on feature extraction,the stacked automatic encoder with robustness was used.In order to improve the ability of gait classification,the sparse coding was used to express and classify the gait features.Experiments results showed the effectiveness of the proposed method in comparison with other state-of-the-art methods on the public databases CASIA-B and TUM-GAID for gait recognition.展开更多
文摘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.
基金Project(51678075) supported by the National Natural Science Foundation of ChinaProject(2017GK2271) supported by Hunan Provincial Science and Technology Department,China
文摘Aiming at the problem of small area human occlusion in gait recognition,a method based on generating adversarial image inpainting network was proposed which can generate a context consistent image for gait occlusion area.In order to reduce the effect of noise on feature extraction,the stacked automatic encoder with robustness was used.In order to improve the ability of gait classification,the sparse coding was used to express and classify the gait features.Experiments results showed the effectiveness of the proposed method in comparison with other state-of-the-art methods on the public databases CASIA-B and TUM-GAID for gait recognition.
