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
网络入侵检测数据呈现高维、非线性和不均衡特点,导致有监督类入侵检测方法泛化能力弱且少数类检测准确率低。针对该问题,文中提出一种联合稀疏自编码器(Sparse Auto-Encoder,SAE),最小极大概率机(Min-Max Probability Machine,MPM)和Ba...网络入侵检测数据呈现高维、非线性和不均衡特点,导致有监督类入侵检测方法泛化能力弱且少数类检测准确率低。针对该问题,文中提出一种联合稀疏自编码器(Sparse Auto-Encoder,SAE),最小极大概率机(Min-Max Probability Machine,MPM)和Bagging集成学习的不均衡样本半监督网络入侵检测方法。首先,采用SAE无监督的学习出原始高维数据的低维隐层特征,以剔除冗余特征并实现数据降维;然后,采用MPM半监督分类器实现对“正常(Normal)”和“异常(Abnormal)”两种网络状态的有效区分;进而,利用K-均值,基于密度的聚类(Density-Based Spatial Clustering of Applications with Noise,DBSCAN)和高斯混合模型(Gaussian Mixture Model,GMM)三种无监督聚类方法对MPM判决为“Abnormal”的数据进行进一步聚类分析;最后,利用Bagging集成学习对三种聚类结果进行综合,从而获得最终的入侵检测结果。同时针对K-均值,DBSCAN和GMM模型参数设置问题,文中提出改进的蚁群算法(Improved Ant Colony Optimization,IACO)进行全局寻优,提升聚类性能。基于KDDCUP99数据集的试验结果表明,相对于两种有监督类方法和一种无监督类方法,所提方法的检测准确率提升超过2.7%,误检率降低超过1.05%,且降低数据获取难度,具有较高的应用前景。展开更多
Pulse pile-up is a problem in nuclear spectroscopy and nuclear reaction studies that occurs when two pulses overlap and distort each other,degrading the quality of energy and timing information.Different methods have ...Pulse pile-up is a problem in nuclear spectroscopy and nuclear reaction studies that occurs when two pulses overlap and distort each other,degrading the quality of energy and timing information.Different methods have been used for pile-up rejection,both digital and analogue,but some pile-up events may contain pulses of interest and need to be reconstructed.The paper proposes a new method for reconstructing pile-up events acquired with a neutron detector array(NEDA)using an one-dimensional convolutional autoencoder(1D-CAE).The datasets for training and testing the 1D-CAE are created from data acquired from the NEDA.The new pile-up signal reconstruction method is evaluated from the point of view of how similar the reconstructed signals are to the original ones.Furthermore,it is analysed considering the result of the neutron-gamma discrimination based on charge comparison,comparing the result obtained from original and reconstructed signals.展开更多
This paper presents an innovative data-integration that uses an iterative-learning method,a deep neural network(DNN)coupled with a stacked autoencoder(SAE)to solve issues encountered with many-objective history matchi...This paper presents an innovative data-integration that uses an iterative-learning method,a deep neural network(DNN)coupled with a stacked autoencoder(SAE)to solve issues encountered with many-objective history matching.The proposed method consists of a DNN-based inverse model with SAE-encoded static data and iterative updates of supervised-learning data are based on distance-based clustering schemes.DNN functions as an inverse model and results in encoded flattened data,while SAE,as a pre-trained neural network,successfully reduces dimensionality and reliably reconstructs geomodels.The iterative-learning method can improve the training data for DNN by showing the error reduction achieved with each iteration step.The proposed workflow shows the small mean absolute percentage error below 4%for all objective functions,while a typical multi-objective evolutionary algorithm fails to significantly reduce the initial population uncertainty.Iterative learning-based manyobjective history matching estimates the trends in water cuts that are not reliably included in dynamicdata matching.This confirms the proposed workflow constructs more plausible geo-models.The workflow would be a reliable alternative to overcome the less-convergent Pareto-based multi-objective evolutionary algorithm in the presence of geological uncertainty and varying objective functions.展开更多
为了提升入侵检测的准确率,鉴于自编码器在学习特征方面的优势以及残差网络在构建深层模型方面的成熟应用,提出一种基于特征降维的改进残差网络入侵检测模型(improved residual network intrusion detection model based on feature dim...为了提升入侵检测的准确率,鉴于自编码器在学习特征方面的优势以及残差网络在构建深层模型方面的成熟应用,提出一种基于特征降维的改进残差网络入侵检测模型(improved residual network intrusion detection model based on feature dimensionality reduction,IRFD),进而缓解传统机器学习入侵检测模型的低准确率问题。IRFD采用堆叠降噪稀疏自编码器策略对数据进行降维,从而提取有效特征。利用卷积注意力机制对残差网络进行改进,构建能提取关键特征的分类网络,并利用两个典型的入侵检测数据集验证IRFD的检测性能。实验结果表明,IRFD在数据集UNSW-NB15和CICIDS 2017上的准确率均达到99%以上,且F1-score分别为99.5%和99.7%。与基线模型相比,提出的IRFD在准确率、精确率和F1-score性能上均有较大提升。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.
文摘网络入侵检测数据呈现高维、非线性和不均衡特点,导致有监督类入侵检测方法泛化能力弱且少数类检测准确率低。针对该问题,文中提出一种联合稀疏自编码器(Sparse Auto-Encoder,SAE),最小极大概率机(Min-Max Probability Machine,MPM)和Bagging集成学习的不均衡样本半监督网络入侵检测方法。首先,采用SAE无监督的学习出原始高维数据的低维隐层特征,以剔除冗余特征并实现数据降维;然后,采用MPM半监督分类器实现对“正常(Normal)”和“异常(Abnormal)”两种网络状态的有效区分;进而,利用K-均值,基于密度的聚类(Density-Based Spatial Clustering of Applications with Noise,DBSCAN)和高斯混合模型(Gaussian Mixture Model,GMM)三种无监督聚类方法对MPM判决为“Abnormal”的数据进行进一步聚类分析;最后,利用Bagging集成学习对三种聚类结果进行综合,从而获得最终的入侵检测结果。同时针对K-均值,DBSCAN和GMM模型参数设置问题,文中提出改进的蚁群算法(Improved Ant Colony Optimization,IACO)进行全局寻优,提升聚类性能。基于KDDCUP99数据集的试验结果表明,相对于两种有监督类方法和一种无监督类方法,所提方法的检测准确率提升超过2.7%,误检率降低超过1.05%,且降低数据获取难度,具有较高的应用前景。
基金partially supported by MICIU MCIN/AEI/10.13039/501100011033Spain with grant PID2020-118265GB-C42,-C44,PRTR-C17.I01+1 种基金Generalitat Valenciana,Spain with grant CIPROM/2022/54,ASFAE/2022/031,CIAPOS/2021/114the EU NextGenerationEU,ESF funds,and the National Science Centre (NCN),Poland (grant No.2020/39/D/ST2/00466)
文摘Pulse pile-up is a problem in nuclear spectroscopy and nuclear reaction studies that occurs when two pulses overlap and distort each other,degrading the quality of energy and timing information.Different methods have been used for pile-up rejection,both digital and analogue,but some pile-up events may contain pulses of interest and need to be reconstructed.The paper proposes a new method for reconstructing pile-up events acquired with a neutron detector array(NEDA)using an one-dimensional convolutional autoencoder(1D-CAE).The datasets for training and testing the 1D-CAE are created from data acquired from the NEDA.The new pile-up signal reconstruction method is evaluated from the point of view of how similar the reconstructed signals are to the original ones.Furthermore,it is analysed considering the result of the neutron-gamma discrimination based on charge comparison,comparing the result obtained from original and reconstructed signals.
基金supported by the basic science research program through the National Research Foundation of Korea(NRF)(2020R1F1A1073395)the basic research project of the Korea Institute of Geoscience and Mineral Resources(KIGAM)(GP2021-011,GP2020-031,21-3117)funded by the Ministry of Science and ICT,Korea。
文摘This paper presents an innovative data-integration that uses an iterative-learning method,a deep neural network(DNN)coupled with a stacked autoencoder(SAE)to solve issues encountered with many-objective history matching.The proposed method consists of a DNN-based inverse model with SAE-encoded static data and iterative updates of supervised-learning data are based on distance-based clustering schemes.DNN functions as an inverse model and results in encoded flattened data,while SAE,as a pre-trained neural network,successfully reduces dimensionality and reliably reconstructs geomodels.The iterative-learning method can improve the training data for DNN by showing the error reduction achieved with each iteration step.The proposed workflow shows the small mean absolute percentage error below 4%for all objective functions,while a typical multi-objective evolutionary algorithm fails to significantly reduce the initial population uncertainty.Iterative learning-based manyobjective history matching estimates the trends in water cuts that are not reliably included in dynamicdata matching.This confirms the proposed workflow constructs more plausible geo-models.The workflow would be a reliable alternative to overcome the less-convergent Pareto-based multi-objective evolutionary algorithm in the presence of geological uncertainty and varying objective functions.
文摘为了提升入侵检测的准确率,鉴于自编码器在学习特征方面的优势以及残差网络在构建深层模型方面的成熟应用,提出一种基于特征降维的改进残差网络入侵检测模型(improved residual network intrusion detection model based on feature dimensionality reduction,IRFD),进而缓解传统机器学习入侵检测模型的低准确率问题。IRFD采用堆叠降噪稀疏自编码器策略对数据进行降维,从而提取有效特征。利用卷积注意力机制对残差网络进行改进,构建能提取关键特征的分类网络,并利用两个典型的入侵检测数据集验证IRFD的检测性能。实验结果表明,IRFD在数据集UNSW-NB15和CICIDS 2017上的准确率均达到99%以上,且F1-score分别为99.5%和99.7%。与基线模型相比,提出的IRFD在准确率、精确率和F1-score性能上均有较大提升。
