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
将权限定义为由访问类型、信息对象、操作范畴和约束条件构成的四元组,并在此基础上建立包含权限编码生成器、权限编码分析器和权限编码库的基于权限四元组的权限控制模型4-TPBAC(4-Tup le Privilege Based Access Con-trol)。介绍了模...将权限定义为由访问类型、信息对象、操作范畴和约束条件构成的四元组,并在此基础上建立包含权限编码生成器、权限编码分析器和权限编码库的基于权限四元组的权限控制模型4-TPBAC(4-Tup le Privilege Based Access Con-trol)。介绍了模型中权限编码生成器和权限编码分析器的工作原理,分析了权限编码分析器中实现页面级权限控制、操作级权限控制和字段级权限控制等控制策略。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.
文摘将权限定义为由访问类型、信息对象、操作范畴和约束条件构成的四元组,并在此基础上建立包含权限编码生成器、权限编码分析器和权限编码库的基于权限四元组的权限控制模型4-TPBAC(4-Tup le Privilege Based Access Con-trol)。介绍了模型中权限编码生成器和权限编码分析器的工作原理,分析了权限编码分析器中实现页面级权限控制、操作级权限控制和字段级权限控制等控制策略。
