We propose a new approach for analyzing the global asymptotic stability of the extended discrete-time bidirectional associative memory （BAM） neural networks. By using the Euler rule, we discretize the continuous-tim...We propose a new approach for analyzing the global asymptotic stability of the extended discrete-time bidirectional associative memory （BAM） neural networks. By using the Euler rule, we discretize the continuous-time BAM neural networks as the extended discrete-time BAM neural networks with non-threshold activation functions. Here we present some conditions under which the neural networks have unique equilibrium points. To judge the global asymptotic stability of the equilibrium points, we introduce a new neural network model - standard neural network model （SNNM）. For the SNNMs, we derive the sufficient conditions for the global asymptotic stability of the equilibrium points, which are formulated as some linear matrix inequalities （LMIs）. We transform the discrete-time BAM into the SNNM and apply the general result about the SNNM to the determination of global asymptotic stability of the discrete-time BAM. The approach proposed extends the known stability results, has lower conservativeness, can be verified easily, and can also be applied to other forms of recurrent neural networks.展开更多
In the fast-evolving landscape of digital networks,the incidence of network intrusions has escalated alarmingly.Simultaneously,the crucial role of time series data in intrusion detection remains largely underappreciat...In the fast-evolving landscape of digital networks,the incidence of network intrusions has escalated alarmingly.Simultaneously,the crucial role of time series data in intrusion detection remains largely underappreciated,with most systems failing to capture the time-bound nuances of network traffic.This leads to compromised detection accuracy and overlooked temporal patterns.Addressing this gap,we introduce a novel SSAE-TCN-BiLSTM(STL)model that integrates time series analysis,significantly enhancing detection capabilities.Our approach reduces feature dimensionalitywith a Stacked Sparse Autoencoder(SSAE)and extracts temporally relevant features through a Temporal Convolutional Network(TCN)and Bidirectional Long Short-term Memory Network(Bi-LSTM).By meticulously adjusting time steps,we underscore the significance of temporal data in bolstering detection accuracy.On the UNSW-NB15 dataset,ourmodel achieved an F1-score of 99.49%,Accuracy of 99.43%,Precision of 99.38%,Recall of 99.60%,and an inference time of 4.24 s.For the CICDS2017 dataset,we recorded an F1-score of 99.53%,Accuracy of 99.62%,Precision of 99.27%,Recall of 99.79%,and an inference time of 5.72 s.These findings not only confirm the STL model’s superior performance but also its operational efficiency,underpinning its significance in real-world cybersecurity scenarios where rapid response is paramount.Our contribution represents a significant advance in cybersecurity,proposing a model that excels in accuracy and adaptability to the dynamic nature of network traffic,setting a new benchmark for intrusion detection systems.展开更多
既有谐波源建模方法在应用于内部拓扑未知与机理不明场景时,难以有效兼顾谐波源的频域稳态特征与动态时变特征,致使所构建模型的准确性与鲁棒性难以提升。为此,该文提出一种基于时-频特征联合提取的谐波源数据驱动建模方法。首先,根据...既有谐波源建模方法在应用于内部拓扑未知与机理不明场景时,难以有效兼顾谐波源的频域稳态特征与动态时变特征,致使所构建模型的准确性与鲁棒性难以提升。为此,该文提出一种基于时-频特征联合提取的谐波源数据驱动建模方法。首先,根据谐波源历史电压电流的频域分量确定谐波源的主导谐波频次;其次,构建谐波源稳态电压时-频特征矩阵并对其进行伪彩色编码以实现特征升维;最后,将动态时变信号与稳态特征分量彩色图组合输入所构建的多重卷积神经网络(multiple convolutional neural network,MCNN)与双向长短时记忆网络(bi-directional long short term memory network,BiLSTM)组合模型,构建反映谐波源动态时变特征与频域稳态特征的电压-电流映射关系。经仿真与实测数据验证,相较于其他数据驱动建模方法,所提方法不仅在单一谐波源建模场景下具有明显的优势,在复杂多谐波源场景下也具备较高的准确率与较强的鲁棒性。展开更多
基金This project was supported by the National Natural Science Foundation of China (60074008) .
文摘We propose a new approach for analyzing the global asymptotic stability of the extended discrete-time bidirectional associative memory （BAM） neural networks. By using the Euler rule, we discretize the continuous-time BAM neural networks as the extended discrete-time BAM neural networks with non-threshold activation functions. Here we present some conditions under which the neural networks have unique equilibrium points. To judge the global asymptotic stability of the equilibrium points, we introduce a new neural network model - standard neural network model （SNNM）. For the SNNMs, we derive the sufficient conditions for the global asymptotic stability of the equilibrium points, which are formulated as some linear matrix inequalities （LMIs）. We transform the discrete-time BAM into the SNNM and apply the general result about the SNNM to the determination of global asymptotic stability of the discrete-time BAM. The approach proposed extends the known stability results, has lower conservativeness, can be verified easily, and can also be applied to other forms of recurrent neural networks.
基金supported in part by the Gansu Province Higher Education Institutions Industrial Support Program:Security Situational Awareness with Artificial Intelligence and Blockchain Technology.Project Number(2020C-29).
文摘In the fast-evolving landscape of digital networks,the incidence of network intrusions has escalated alarmingly.Simultaneously,the crucial role of time series data in intrusion detection remains largely underappreciated,with most systems failing to capture the time-bound nuances of network traffic.This leads to compromised detection accuracy and overlooked temporal patterns.Addressing this gap,we introduce a novel SSAE-TCN-BiLSTM(STL)model that integrates time series analysis,significantly enhancing detection capabilities.Our approach reduces feature dimensionalitywith a Stacked Sparse Autoencoder(SSAE)and extracts temporally relevant features through a Temporal Convolutional Network(TCN)and Bidirectional Long Short-term Memory Network(Bi-LSTM).By meticulously adjusting time steps,we underscore the significance of temporal data in bolstering detection accuracy.On the UNSW-NB15 dataset,ourmodel achieved an F1-score of 99.49%,Accuracy of 99.43%,Precision of 99.38%,Recall of 99.60%,and an inference time of 4.24 s.For the CICDS2017 dataset,we recorded an F1-score of 99.53%,Accuracy of 99.62%,Precision of 99.27%,Recall of 99.79%,and an inference time of 5.72 s.These findings not only confirm the STL model’s superior performance but also its operational efficiency,underpinning its significance in real-world cybersecurity scenarios where rapid response is paramount.Our contribution represents a significant advance in cybersecurity,proposing a model that excels in accuracy and adaptability to the dynamic nature of network traffic,setting a new benchmark for intrusion detection systems.
文摘既有谐波源建模方法在应用于内部拓扑未知与机理不明场景时,难以有效兼顾谐波源的频域稳态特征与动态时变特征,致使所构建模型的准确性与鲁棒性难以提升。为此,该文提出一种基于时-频特征联合提取的谐波源数据驱动建模方法。首先,根据谐波源历史电压电流的频域分量确定谐波源的主导谐波频次;其次,构建谐波源稳态电压时-频特征矩阵并对其进行伪彩色编码以实现特征升维;最后,将动态时变信号与稳态特征分量彩色图组合输入所构建的多重卷积神经网络(multiple convolutional neural network,MCNN)与双向长短时记忆网络(bi-directional long short term memory network,BiLSTM)组合模型,构建反映谐波源动态时变特征与频域稳态特征的电压-电流映射关系。经仿真与实测数据验证,相较于其他数据驱动建模方法,所提方法不仅在单一谐波源建模场景下具有明显的优势,在复杂多谐波源场景下也具备较高的准确率与较强的鲁棒性。
