Encrypted traffic identification pertains to the precise acquisition and categorization of data from traffic datasets containing imbalanced and obscured content.The extraction of encrypted traffic attributes and their...Encrypted traffic identification pertains to the precise acquisition and categorization of data from traffic datasets containing imbalanced and obscured content.The extraction of encrypted traffic attributes and their subsequent identification presents a formidable challenge.The existing models have predominantly relied on direct extraction of encrypted traffic data from imbalanced datasets,with the dataset’s imbalance significantly affecting the model’s performance.In the present study,a new model,referred to as UD-VLD(Unbalanced Dataset-VAE-LSTM-DRN),was proposed to address above problem.The proposed model is an encrypted traffic identification model for handling unbalanced datasets.The encoder of the variational autoencoder(VAE)is combined with the decoder and Long-short term Memory(LSTM)in UD-VLD model to realize the data enhancement processing of the original unbalanced datasets.The enhanced data is processed by transforming the deep residual network(DRN)to address neural network gradient-related issues.Subsequently,the data is classified and recognized.The UD-VLD model integrates the related techniques of deep learning into the encrypted traffic recognition technique,thereby solving the processing problem for unbalanced datasets.The UD-VLD model was tested using the publicly available Tor dataset and VPN dataset.The UD-VLD model is evaluated against other comparative models in terms of accuracy,loss rate,precision,recall,F1-score,total time,and ROC curve.The results reveal that the UD-VLD model exhibits better performance in both binary and multi classification,being higher than other encrypted traffic recognition models that exist for unbalanced datasets.Furthermore,the evaluation performance indicates that the UD-VLD model effectivelymitigates the impact of unbalanced data on traffic classification.and can serve as a novel solution for encrypted traffic identification.展开更多
在2024中国国际胶黏剂及密封剂展览会(ASE China 2024)上,瓦克展示了供纺织贴合、纸品包装、木工及地毯背衬各类胶黏剂使用的乙烯-醋酸乙烯共聚物(VAE)乳液产品,包括首次在展会亮相的VINNAPAS威耐实EP 712,以及本地化的干燥性能突出的VI...在2024中国国际胶黏剂及密封剂展览会(ASE China 2024)上,瓦克展示了供纺织贴合、纸品包装、木工及地毯背衬各类胶黏剂使用的乙烯-醋酸乙烯共聚物(VAE)乳液产品,包括首次在展会亮相的VINNAPAS威耐实EP 712,以及本地化的干燥性能突出的VINNAPAS威耐实EP 760。这两款产品可以分别提升不同胶黏剂产品的性能,如更优异的耐水性能、更加出色的黏结力,以及更快的固化速率等,为下游行业提供环保创新的解决方案。同期,瓦克还展出了一系列应用于建筑、工业等领域的高性能GENIOSIL硅烷改性聚合物。展开更多
由于核反应堆发电的特殊性,核电厂对于生产安全的敏感度远胜于普通电厂。作为日常运维的重要环节,核电机组运行状态监测,对于核电厂的安全稳定运行具有重要意义。当前核电机组状态监测主要采用预设固定阈值报警结合人工监盘的方式,这种...由于核反应堆发电的特殊性,核电厂对于生产安全的敏感度远胜于普通电厂。作为日常运维的重要环节,核电机组运行状态监测,对于核电厂的安全稳定运行具有重要意义。当前核电机组状态监测主要采用预设固定阈值报警结合人工监盘的方式,这种方式无法发现低于报警阈值的异常状态,同时存在一定程度的漏报风险。核电运行数据作为高维海量时序数据,具有正常样本和异常样本分布不均衡以及数据缺乏标签的问题,这限制了有监督深度学习方法的使用。提出了一种基于变分自编码器(variational autoencoders,VAE)构建的无监督深度学习模型对真实运行数据进行异常检测,通过正常运行数据学习正常模式下数据在隐空间的分布,并基于异常数据无法被良好重构的原理,通过重构误差的大小来判别当前状态是否异常。实验以核电机组化学和容积控制系统(removal-chemical and volume control system,RCV)中的上充泵为例,使用真实运行数据结合插入异常的方式对模型进行了验证,并与经典机器学习方法进行了对比。实验结果表明基于变分自编码器的模型能够有效检测到核电真实数据中的异常数据片段及离群点,检测精确率和召回率均高于90%,检测性能相对孤立森林和支持向量机等经典机器学习算法具有优势,具备一定的实用价值和研究意义。展开更多
针对滚动轴承故障诊断中样本分布不均衡引起的模型泛化能力差、诊断精度低的问题,从两个方面展开研究:(1)故障样本增广,提出结合变分自编码器(VAE)和生成对抗网络(GAN)的VAE-GAN样本增广模型;(2)改进分类算法,提出基于焦点损失(FL)和卷...针对滚动轴承故障诊断中样本分布不均衡引起的模型泛化能力差、诊断精度低的问题,从两个方面展开研究:(1)故障样本增广,提出结合变分自编码器(VAE)和生成对抗网络(GAN)的VAE-GAN样本增广模型;(2)改进分类算法,提出基于焦点损失(FL)和卷积神经网络(CNN)的FLCNN(focal loss and convolutional neural network)样本分类模型。在此基础上,将VAE-GAN和FLCNN融合,构建VAE-GAN+FLCNN轴承故障诊断模型。首先,将样本量少的故障类输入VAE-GAN模型,通过交替训练编码网络、生成网络和判别网络,学习出真实故障样本的数据分布,从而实现故障样本的增广;然后用增广后的数据样本训练FLCNN分类模型,完成轴承故障识别。试验对比结果表明,所提方法能够有效提升样本不均衡条件下的轴承故障诊断效果,拥有更高的Recall值和F1-score值。展开更多
基金supported by the Fundamental Research Funds for Higher Education Institutions of Heilongjiang Province(145209126)the Heilongjiang Province Higher Education Teaching Reform Project under Grant No.SJGY20200770.
文摘Encrypted traffic identification pertains to the precise acquisition and categorization of data from traffic datasets containing imbalanced and obscured content.The extraction of encrypted traffic attributes and their subsequent identification presents a formidable challenge.The existing models have predominantly relied on direct extraction of encrypted traffic data from imbalanced datasets,with the dataset’s imbalance significantly affecting the model’s performance.In the present study,a new model,referred to as UD-VLD(Unbalanced Dataset-VAE-LSTM-DRN),was proposed to address above problem.The proposed model is an encrypted traffic identification model for handling unbalanced datasets.The encoder of the variational autoencoder(VAE)is combined with the decoder and Long-short term Memory(LSTM)in UD-VLD model to realize the data enhancement processing of the original unbalanced datasets.The enhanced data is processed by transforming the deep residual network(DRN)to address neural network gradient-related issues.Subsequently,the data is classified and recognized.The UD-VLD model integrates the related techniques of deep learning into the encrypted traffic recognition technique,thereby solving the processing problem for unbalanced datasets.The UD-VLD model was tested using the publicly available Tor dataset and VPN dataset.The UD-VLD model is evaluated against other comparative models in terms of accuracy,loss rate,precision,recall,F1-score,total time,and ROC curve.The results reveal that the UD-VLD model exhibits better performance in both binary and multi classification,being higher than other encrypted traffic recognition models that exist for unbalanced datasets.Furthermore,the evaluation performance indicates that the UD-VLD model effectivelymitigates the impact of unbalanced data on traffic classification.and can serve as a novel solution for encrypted traffic identification.
文摘在2024中国国际胶黏剂及密封剂展览会(ASE China 2024)上,瓦克展示了供纺织贴合、纸品包装、木工及地毯背衬各类胶黏剂使用的乙烯-醋酸乙烯共聚物(VAE)乳液产品,包括首次在展会亮相的VINNAPAS威耐实EP 712,以及本地化的干燥性能突出的VINNAPAS威耐实EP 760。这两款产品可以分别提升不同胶黏剂产品的性能,如更优异的耐水性能、更加出色的黏结力,以及更快的固化速率等,为下游行业提供环保创新的解决方案。同期,瓦克还展出了一系列应用于建筑、工业等领域的高性能GENIOSIL硅烷改性聚合物。
文摘由于核反应堆发电的特殊性,核电厂对于生产安全的敏感度远胜于普通电厂。作为日常运维的重要环节,核电机组运行状态监测,对于核电厂的安全稳定运行具有重要意义。当前核电机组状态监测主要采用预设固定阈值报警结合人工监盘的方式,这种方式无法发现低于报警阈值的异常状态,同时存在一定程度的漏报风险。核电运行数据作为高维海量时序数据,具有正常样本和异常样本分布不均衡以及数据缺乏标签的问题,这限制了有监督深度学习方法的使用。提出了一种基于变分自编码器(variational autoencoders,VAE)构建的无监督深度学习模型对真实运行数据进行异常检测,通过正常运行数据学习正常模式下数据在隐空间的分布,并基于异常数据无法被良好重构的原理,通过重构误差的大小来判别当前状态是否异常。实验以核电机组化学和容积控制系统(removal-chemical and volume control system,RCV)中的上充泵为例,使用真实运行数据结合插入异常的方式对模型进行了验证,并与经典机器学习方法进行了对比。实验结果表明基于变分自编码器的模型能够有效检测到核电真实数据中的异常数据片段及离群点,检测精确率和召回率均高于90%,检测性能相对孤立森林和支持向量机等经典机器学习算法具有优势,具备一定的实用价值和研究意义。
文摘针对滚动轴承故障诊断中样本分布不均衡引起的模型泛化能力差、诊断精度低的问题,从两个方面展开研究:(1)故障样本增广,提出结合变分自编码器(VAE)和生成对抗网络(GAN)的VAE-GAN样本增广模型;(2)改进分类算法,提出基于焦点损失(FL)和卷积神经网络(CNN)的FLCNN(focal loss and convolutional neural network)样本分类模型。在此基础上,将VAE-GAN和FLCNN融合,构建VAE-GAN+FLCNN轴承故障诊断模型。首先,将样本量少的故障类输入VAE-GAN模型,通过交替训练编码网络、生成网络和判别网络,学习出真实故障样本的数据分布,从而实现故障样本的增广;然后用增广后的数据样本训练FLCNN分类模型,完成轴承故障识别。试验对比结果表明,所提方法能够有效提升样本不均衡条件下的轴承故障诊断效果,拥有更高的Recall值和F1-score值。
