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
在基于人工免疫的入侵检测研究领域,一般都是应用随机产生字符串的方法来生成检测器。这种方法生成检测器的速度较慢,而且生成的检测器集的检测率低。由于非我样本中存在着关于非我空间的信息,提出通过应用非我样本来初始化基因库并应...在基于人工免疫的入侵检测研究领域,一般都是应用随机产生字符串的方法来生成检测器。这种方法生成检测器的速度较慢,而且生成的检测器集的检测率低。由于非我样本中存在着关于非我空间的信息,提出通过应用非我样本来初始化基因库并应用基因库来生成检测器的方法来检测入侵。应用KDD Cup 1999入侵检测数据集,通过实验证明该方法是有效的,能更快地生成检测率更高的检测器集。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.
文摘在基于人工免疫的入侵检测研究领域,一般都是应用随机产生字符串的方法来生成检测器。这种方法生成检测器的速度较慢,而且生成的检测器集的检测率低。由于非我样本中存在着关于非我空间的信息,提出通过应用非我样本来初始化基因库并应用基因库来生成检测器的方法来检测入侵。应用KDD Cup 1999入侵检测数据集,通过实验证明该方法是有效的,能更快地生成检测率更高的检测器集。
