Intrusion detection systems(IDS)can play a significant role in detecting security threats or malicious attacks that aim to steal information and/or corrupt network protocols.To deal with the dynamic and complex nature...Intrusion detection systems(IDS)can play a significant role in detecting security threats or malicious attacks that aim to steal information and/or corrupt network protocols.To deal with the dynamic and complex nature of cyber-attacks,advanced intelligent tools have been applied resulting into powerful and automated IDS that rely on the latest advances of machine learning(ML)and deep learning(DL).Most of the reported effort has been devoted on building complex ML/DL architectures adopting a brute force approach towards the maximization of their detection capacity.However,just a limited number of studies have focused on the identification or extraction of user-friendly risk indicators that could be easily used by security experts.Many papers have explored various dimensionality reduction algorithms,however a large number of selected features is still required to detect the attacks successfully,which humans cannot intuitively or immediately understand.To enhance user’s trust and understanding on data without sacrificing on accuracy,this paper contributes to the transformation of the available data collected by IDS into a single actionable and easy-to-understand risk indicator.To achieve this,a novel feature extraction pipeline was implemented consisting of the following components:(i)a fuzzy allocation scheme that transforms raw data to fuzzy class memberships,(ii)a novel modality transformation mechanism for converting feature vectors to images(Vec2im)and(iii)a dimensionality reduction module that makes use of Siamese convolutional neural networks that finally reduces the input data dimensionality into a 1-d feature space.The performance of the proposed methodology was validated with respect to detection accuracy,dimensionality reduction performance and execution time on the NSL-KDD dataset via a thorough comparative analysis that demonstrated its effectiveness(86.64%testing accuracy using only one feature)over a number of well-known feature selection(FS)and extraction techniques.The output of the proposed feature extraction pipeline could be potentially used by security experts as an indicator of malicious activity,whereas the generated images could be further utilized and/or integrated as a visual analytics tool in existing IDS.展开更多
Intrusion detection systems(IDS)can play a significant role in detecting security threats or malicious attacks that aim to steal information and/or corrupt network protocols.To deal with the dynamic and complex nature...Intrusion detection systems(IDS)can play a significant role in detecting security threats or malicious attacks that aim to steal information and/or corrupt network protocols.To deal with the dynamic and complex nature of cyber-attacks,advanced intelligent tools have been applied resulting into powerful and automated IDS that rely on the latest advances of machine learning(ML)and deep learning(DL).Most of the reported effort has been devoted on building complex ML/DL architectures adopting a brute force approach towards the maximization of their detection capacity.However,just a limited number of studies have focused on the identification or extraction of user-friendly risk indicators that could be easily used by security experts.Many papers have explored various dimensionality reduction algorithms,however a large number of selected features is still required to detect the attacks successfully,which humans cannot intuitively or immediately understand.To enhance user’s trust and understanding on data without sacrificing on accuracy,this paper contributes to the transformation of the available data collected by IDS into a single actionable and easy-to-understand risk indicator.To achieve this,a novel feature extraction pipeline was implemented consisting of the following components:(i)a fuzzy allocation scheme that transforms raw data to fuzzy class memberships,(ii)a novel modality transformation mechanism for converting feature vectors to images(Vec2im)and(iii)a dimensionality reduction module that makes use of Siamese convolutional neural networks that finally reduces the input data dimensionality into a 1-d feature space.The performance of the proposed methodology was validated with respect to detection accuracy,dimensionality reduction performance and execution time on the NSL-KDD dataset via a thorough comparative analysis that demonstrated its effectiveness(86.64%testing accuracy using only one feature)over a number of well-known feature selection(FS)and extraction techniques.The output of the proposed feature extraction pipeline could be potentially used by security experts as an indicator of malicious activity,whereas the generated images could be further utilized and/or integrated as a visual analytics tool in existing IDS.展开更多
轴承故障类型复杂,并且在不同工况下每种故障类型都很难获得足够的训练样本。因此,本文提出一种基于深度神经网络的小样本学习分类算法,引入第1层具有宽卷积核网络(Convolutional neural network with training interference,TICNN)作...轴承故障类型复杂,并且在不同工况下每种故障类型都很难获得足够的训练样本。因此,本文提出一种基于深度神经网络的小样本学习分类算法,引入第1层具有宽卷积核网络(Convolutional neural network with training interference,TICNN)作为孪生网络的子网络用于提取特征,减少工业环境噪声影响。孪生网络是一种常用于小样本学习的结构,通过输入相同或不同类别的样本对进行训练,学习不同属性样本与特征之间的映射关系,并采用相似度进行度量。测试样本通过寻找最近邻的类别来实现分类。在标准凯斯西储大学轴承故障诊断基准数据集上的实验结果表明,在数据有限的情况下,本文模型在故障诊断中表现出更好的效果。当使用最少的训练数据在不同的噪声环境中进行测试时,本文小样本学习模型的性能超过了具有合理噪声水平的基线模型,故障诊断准确率达到了94.41%。当在具有新故障类型或新工作条件的测试集上进行评估时,本文模型仍然有效。展开更多
针对推荐系统中双塔型神经网络难以学习用户侧和商品侧交互信息以及图连接信息的问题,提出一种二阶段孪生卷积神经网络推荐算法(TSN)。首先,以用户行为构建异质图;然后,在双塔型神经网络之间设计图卷积孪生网络,从而在学习异质图连接信...针对推荐系统中双塔型神经网络难以学习用户侧和商品侧交互信息以及图连接信息的问题,提出一种二阶段孪生卷积神经网络推荐算法(TSN)。首先,以用户行为构建异质图;然后,在双塔型神经网络之间设计图卷积孪生网络,从而在学习异质图连接信息的同时进行信息交互;最后,通过设计特殊结构的二阶段孪生信息共享机制,使得用户侧和商品侧的神经网络在训练过程中能够动态地、双向地传输信息,且有效避免神经网络串联。在基于MovieLens和豆瓣电影数据集的对比实验中,NDCG@10、NDCG@50、NDCG@100相较于最优基准算法DAT(Dual Augmented Two-tower model for online large-scale recommendation)提升了11.39%~23.98%。结果表明,所提算法能够缓解双塔型神经网络缺乏信息交互的问题,较对比算法推荐性能提升显著。展开更多
基金This work has received funding from the European Community’s H2020 Programme,under grant agreement Nr.826183(SPHINX).
文摘Intrusion detection systems(IDS)can play a significant role in detecting security threats or malicious attacks that aim to steal information and/or corrupt network protocols.To deal with the dynamic and complex nature of cyber-attacks,advanced intelligent tools have been applied resulting into powerful and automated IDS that rely on the latest advances of machine learning(ML)and deep learning(DL).Most of the reported effort has been devoted on building complex ML/DL architectures adopting a brute force approach towards the maximization of their detection capacity.However,just a limited number of studies have focused on the identification or extraction of user-friendly risk indicators that could be easily used by security experts.Many papers have explored various dimensionality reduction algorithms,however a large number of selected features is still required to detect the attacks successfully,which humans cannot intuitively or immediately understand.To enhance user’s trust and understanding on data without sacrificing on accuracy,this paper contributes to the transformation of the available data collected by IDS into a single actionable and easy-to-understand risk indicator.To achieve this,a novel feature extraction pipeline was implemented consisting of the following components:(i)a fuzzy allocation scheme that transforms raw data to fuzzy class memberships,(ii)a novel modality transformation mechanism for converting feature vectors to images(Vec2im)and(iii)a dimensionality reduction module that makes use of Siamese convolutional neural networks that finally reduces the input data dimensionality into a 1-d feature space.The performance of the proposed methodology was validated with respect to detection accuracy,dimensionality reduction performance and execution time on the NSL-KDD dataset via a thorough comparative analysis that demonstrated its effectiveness(86.64%testing accuracy using only one feature)over a number of well-known feature selection(FS)and extraction techniques.The output of the proposed feature extraction pipeline could be potentially used by security experts as an indicator of malicious activity,whereas the generated images could be further utilized and/or integrated as a visual analytics tool in existing IDS.
基金received funding from the European Community’s H2020 Programme,under grant agreement Nr.826183(SPHINX).
文摘Intrusion detection systems(IDS)can play a significant role in detecting security threats or malicious attacks that aim to steal information and/or corrupt network protocols.To deal with the dynamic and complex nature of cyber-attacks,advanced intelligent tools have been applied resulting into powerful and automated IDS that rely on the latest advances of machine learning(ML)and deep learning(DL).Most of the reported effort has been devoted on building complex ML/DL architectures adopting a brute force approach towards the maximization of their detection capacity.However,just a limited number of studies have focused on the identification or extraction of user-friendly risk indicators that could be easily used by security experts.Many papers have explored various dimensionality reduction algorithms,however a large number of selected features is still required to detect the attacks successfully,which humans cannot intuitively or immediately understand.To enhance user’s trust and understanding on data without sacrificing on accuracy,this paper contributes to the transformation of the available data collected by IDS into a single actionable and easy-to-understand risk indicator.To achieve this,a novel feature extraction pipeline was implemented consisting of the following components:(i)a fuzzy allocation scheme that transforms raw data to fuzzy class memberships,(ii)a novel modality transformation mechanism for converting feature vectors to images(Vec2im)and(iii)a dimensionality reduction module that makes use of Siamese convolutional neural networks that finally reduces the input data dimensionality into a 1-d feature space.The performance of the proposed methodology was validated with respect to detection accuracy,dimensionality reduction performance and execution time on the NSL-KDD dataset via a thorough comparative analysis that demonstrated its effectiveness(86.64%testing accuracy using only one feature)over a number of well-known feature selection(FS)and extraction techniques.The output of the proposed feature extraction pipeline could be potentially used by security experts as an indicator of malicious activity,whereas the generated images could be further utilized and/or integrated as a visual analytics tool in existing IDS.
文摘轴承故障类型复杂,并且在不同工况下每种故障类型都很难获得足够的训练样本。因此,本文提出一种基于深度神经网络的小样本学习分类算法,引入第1层具有宽卷积核网络(Convolutional neural network with training interference,TICNN)作为孪生网络的子网络用于提取特征,减少工业环境噪声影响。孪生网络是一种常用于小样本学习的结构,通过输入相同或不同类别的样本对进行训练,学习不同属性样本与特征之间的映射关系,并采用相似度进行度量。测试样本通过寻找最近邻的类别来实现分类。在标准凯斯西储大学轴承故障诊断基准数据集上的实验结果表明,在数据有限的情况下,本文模型在故障诊断中表现出更好的效果。当使用最少的训练数据在不同的噪声环境中进行测试时,本文小样本学习模型的性能超过了具有合理噪声水平的基线模型,故障诊断准确率达到了94.41%。当在具有新故障类型或新工作条件的测试集上进行评估时,本文模型仍然有效。
文摘针对推荐系统中双塔型神经网络难以学习用户侧和商品侧交互信息以及图连接信息的问题,提出一种二阶段孪生卷积神经网络推荐算法(TSN)。首先,以用户行为构建异质图;然后,在双塔型神经网络之间设计图卷积孪生网络,从而在学习异质图连接信息的同时进行信息交互;最后,通过设计特殊结构的二阶段孪生信息共享机制,使得用户侧和商品侧的神经网络在训练过程中能够动态地、双向地传输信息,且有效避免神经网络串联。在基于MovieLens和豆瓣电影数据集的对比实验中,NDCG@10、NDCG@50、NDCG@100相较于最优基准算法DAT(Dual Augmented Two-tower model for online large-scale recommendation)提升了11.39%~23.98%。结果表明,所提算法能够缓解双塔型神经网络缺乏信息交互的问题,较对比算法推荐性能提升显著。
