The rapid growth and uptake of network-based communication technologies have made cybersecurity a significant challenge as the number of cyber-attacks is also increasing.A number of detection systems are used in an at...The rapid growth and uptake of network-based communication technologies have made cybersecurity a significant challenge as the number of cyber-attacks is also increasing.A number of detection systems are used in an attempt to detect known attacks using signatures in network traffic.In recent years,researchers have used different machine learning methods to detect network attacks without relying on those signatures.The methods generally have a high false-positive rate which is not adequate for an industry-ready intrusion detection product.In this study,we propose and implement a new method that relies on a modular deep neural network for reducing the false positive rate in the XSS attack detection system.Experiments were performed using a dataset consists of 1000 malicious and 10000 benign sample.The model uses 50 features selected by using Pearson correlation method and will be used in the detection and preventions of XSS attacks.The results obtained from the experiments depict improvement in the detection accuracy as high as 99.96%compared to other approaches.展开更多
This work explores the inverse stochastic resonance(ISR) induced by bounded noise and the multiple inverse stochastic resonance induced by time delay by constructing a modular neural network, where the modified Oja’s...This work explores the inverse stochastic resonance(ISR) induced by bounded noise and the multiple inverse stochastic resonance induced by time delay by constructing a modular neural network, where the modified Oja’s synaptic learning rule is employed to characterize synaptic plasticity in this network. Meanwhile, the effects of synaptic plasticity on the ISR dynamics are investigated. Through numerical simulations, it is found that the mean firing rate curve under the influence of bounded noise has an inverted bell-like shape, which implies the appearance of ISR. Moreover, synaptic plasticity with smaller learning rate strengthens this ISR phenomenon, while synaptic plasticity with larger learning rate weakens or even destroys it. On the other hand, the mean firing rate curve under the influence of time delay is found to exhibit a decaying oscillatory process, which represents the emergence of multiple ISR. However, the multiple ISR phenomenon gradually weakens until it disappears with increasing noise amplitude. On the same time, synaptic plasticity with smaller learning rate also weakens this multiple ISR phenomenon, while synaptic plasticity with larger learning rate strengthens it. Furthermore, we find that changes of synaptic learning rate can induce the emergence of ISR phenomenon. We hope these obtained results would provide new insights into the study of ISR in neuroscience.展开更多
The Internet of Things(IoT)has led to rapid growth in smart cities.However,IoT botnet-based attacks against smart city systems are becoming more prevalent.Detection methods for IoT botnet-based attacks have been the s...The Internet of Things(IoT)has led to rapid growth in smart cities.However,IoT botnet-based attacks against smart city systems are becoming more prevalent.Detection methods for IoT botnet-based attacks have been the subject of extensive research,but the identification of early-stage behaviour of the IoT botnet prior to any attack remains a largely unexplored area that could prevent any attack before it is launched.Few studies have addressed the early stages of IoT botnet detection using monolithic deep learning algorithms that could require more time for training and detection.We,however,propose an edge-based deep learning system for the detection of the early stages of IoT botnets in smart cities.The proposed system,which we call EDIT(Edge-based Detection of early-stage IoT Botnet),aims to detect abnormalities in network communication traffic caused by early-stage IoT botnets based on the modular neural network(MNN)method at multi-access edge computing(MEC)servers.MNN can improve detection accuracy and efficiency by leveraging parallel computing on MEC.According to the findings,EDIT has a lower false-negative rate compared to a monolithic approach and other studies.At the MEC server,EDIT takes as little as 16 ms for the detection of an IoT botnet.展开更多
Air pollution control poses a major problem in the implementation of municipal solid waste incineration(MSWI).Accurate prediction of nitrogen oxides(NO_(x))concentration plays an important role in efficient NO_(x)emis...Air pollution control poses a major problem in the implementation of municipal solid waste incineration(MSWI).Accurate prediction of nitrogen oxides(NO_(x))concentration plays an important role in efficient NO_(x)emission controlling.In this study,a modular long short-term memory(M-LSTM)network is developed to design an efficient prediction model for NO_(x)concentration.First,the fuzzy C means(FCM)algorithm is utilized to divide the task into several sub-tasks,aiming to realize the divide-and-conquer ability for complex task.Second,long short-term memory(LSTM)neural networks are applied to tackle corresponding sub-tasks,which can improve the prediction accuracy of the sub-networks.Third,a cooperative decision strategy is designed to guarantee the generalization performance during the testing or application stage.Finally,after being evaluated by a benchmark simulation,the proposed method is applied to a real MSWI process.And the experimental results demonstrate the considerable prediction ability of the M-LSTM network.展开更多
Time delay and coupling strength are important factors that affect the synchronization of neural networks.In this study,a modular neural network containing subnetworks of different scales was constructed using the Hod...Time delay and coupling strength are important factors that affect the synchronization of neural networks.In this study,a modular neural network containing subnetworks of different scales was constructed using the Hodgkin–Huxley(HH)neural model;i.e.,a small-scale random network was unidirectionally connected to a large-scale small-world network through chemical synapses.Time delays were found to induce multiple synchronization transitions in the network.An increase in coupling strength also promoted synchronization of the network when the time delay was an integer multiple of the firing period of a single neuron.Considering that time delays at different locations in a modular network may have different effects,we explored the influence of time delays within each subnetwork and between two subnetworks on the synchronization of modular networks.We found that when the subnetworks were well synchronized internally,an increase in the time delay within both subnetworks induced multiple synchronization transitions of their own.In addition,the synchronization state of the small-scale network affected the synchronization of the large-scale network.It was surprising to find that an increase in the time delay between the two subnetworks caused the synchronization factor of the modular network to vary periodically,but it had essentially no effect on the synchronization within the receiving subnetwork.By analyzing the phase difference between the two subnetworks,we found that the mechanism of the periodic variation of the synchronization factor of the modular network was the periodic variation of the phase difference.Finally,the generality of the results was demonstrated by investigating modular networks at different scales.展开更多
In terms of the modular fuzzy neural network (MFNN) combining fuzzy c-mean (FCM) cluster and single-layer neural network, a short-term climate prediction model is developed. It is found from modeling results that the ...In terms of the modular fuzzy neural network (MFNN) combining fuzzy c-mean (FCM) cluster and single-layer neural network, a short-term climate prediction model is developed. It is found from modeling results that the MFNN model for short-term climate prediction has advantages of simple structure, no hidden layer and stable network parameters because of the assembling of sound functions of the self-adaptive learning, association and fuzzy information processing of fuzzy mathematics and neural network methods. The case computational results of Guangxi flood season (JJA) rainfall show that the mean absolute error (MAE) and mean relative error (MRE) of the prediction during 1998-2002 are 68.8 mm and 9.78%, and in comparison with the regression method, under the conditions of the same predictors and period they are 97.8 mm and 12.28% respectively. Furthermore, it is also found from the stability analysis of the modular model that the change of the prediction results of independent samples with training times in the stably convergent interval of the model is less than 1.3 mm. The obvious oscillation phenomenon of prediction results with training times, such as in the common back-propagation neural network (BPNN) model, does not occur, indicating a better practical application potential of the MFNN model.展开更多
The magnetic compensation of aeromagnetic survey is an important calibration work,which has a great impact on the accuracy of measurement.In an aeromagnetic survey flight,measurement data consists of diurnal variation...The magnetic compensation of aeromagnetic survey is an important calibration work,which has a great impact on the accuracy of measurement.In an aeromagnetic survey flight,measurement data consists of diurnal variation,aircraft maneuver interference field,and geomagnetic field.In this paper,appropriate physical features and the modular feedforward neural network(MFNN)with Levenberg-Marquard(LM)back propagation algorithm are adopted to supervised learn fluctuation of measuring signals and separate the interference magnetic field from the measurement data.LM algorithm is a kind of least square estimation algorithm of nonlinear parameters.It iteratively calculates the jacobian matrix of error performance and the adjustment value of gradient with the regularization method.LM algorithm’s computing efficiency is high and fitting error is very low.The fitting performance and the compensation accuracy of LM-MFNN algorithm are proved to be much better than those of TOLLES-LAWSON(T-L)model with the linear least square(LS)solution by fitting experiments with five different aeromagnetic surveys’data.展开更多
In vitro neural networks offer a simplified model to study brain nervous system functions and represent a vital platform for investigating cerebral neural activities.Microelectrode array(MEA)chips are commonly used to...In vitro neural networks offer a simplified model to study brain nervous system functions and represent a vital platform for investigating cerebral neural activities.Microelectrode array(MEA)chips are commonly used to construct modular neural networks and enable electrical stimulation and recording for uncovering signal generation and conduction mechanisms.However,conventional two-dimensional(2D)MEA chips face significant limitations,including restricted neuronal growth dimensions and insufficient neuron density.Herein,we present a novel micro-integrated chip featuring a three-dimensional(3D)physical microtube array that facilitates the regulated,confined growth of neurons.The microtube array not only provides a 3D microenvironment for neuronal growth and differentiation but also enhances neuronal network density and structural organization.Furthermore,by integrating the microtube array with a customized MEA,precise electrical stimulation can be applied to modular neural networks.Experimental results demonstrate that electrical stimulation effectively promotes the formation of connection pathways between adjacent 3D neural networks.Variable-parameter electrical stimulation experiments reveal that increasing voltage enhances the Young’s modulus of neurons,highlighting the method’s role in supporting the stable development of neuronal networks.This modular culture platform,combined with precise electrical stimulation,paves the way for constructing high-density 3D neuronal networks and enables synchronous control of modular neural activities.The proposed approach holds significant potential for advancing applications in neuroscience,tissue engineering,and organ-onchip technologies.展开更多
文摘The rapid growth and uptake of network-based communication technologies have made cybersecurity a significant challenge as the number of cyber-attacks is also increasing.A number of detection systems are used in an attempt to detect known attacks using signatures in network traffic.In recent years,researchers have used different machine learning methods to detect network attacks without relying on those signatures.The methods generally have a high false-positive rate which is not adequate for an industry-ready intrusion detection product.In this study,we propose and implement a new method that relies on a modular deep neural network for reducing the false positive rate in the XSS attack detection system.Experiments were performed using a dataset consists of 1000 malicious and 10000 benign sample.The model uses 50 features selected by using Pearson correlation method and will be used in the detection and preventions of XSS attacks.The results obtained from the experiments depict improvement in the detection accuracy as high as 99.96%compared to other approaches.
基金the National Natural Science Foundation of China(Grant No.11972217).
文摘This work explores the inverse stochastic resonance(ISR) induced by bounded noise and the multiple inverse stochastic resonance induced by time delay by constructing a modular neural network, where the modified Oja’s synaptic learning rule is employed to characterize synaptic plasticity in this network. Meanwhile, the effects of synaptic plasticity on the ISR dynamics are investigated. Through numerical simulations, it is found that the mean firing rate curve under the influence of bounded noise has an inverted bell-like shape, which implies the appearance of ISR. Moreover, synaptic plasticity with smaller learning rate strengthens this ISR phenomenon, while synaptic plasticity with larger learning rate weakens or even destroys it. On the other hand, the mean firing rate curve under the influence of time delay is found to exhibit a decaying oscillatory process, which represents the emergence of multiple ISR. However, the multiple ISR phenomenon gradually weakens until it disappears with increasing noise amplitude. On the same time, synaptic plasticity with smaller learning rate also weakens this multiple ISR phenomenon, while synaptic plasticity with larger learning rate strengthens it. Furthermore, we find that changes of synaptic learning rate can induce the emergence of ISR phenomenon. We hope these obtained results would provide new insights into the study of ISR in neuroscience.
文摘The Internet of Things(IoT)has led to rapid growth in smart cities.However,IoT botnet-based attacks against smart city systems are becoming more prevalent.Detection methods for IoT botnet-based attacks have been the subject of extensive research,but the identification of early-stage behaviour of the IoT botnet prior to any attack remains a largely unexplored area that could prevent any attack before it is launched.Few studies have addressed the early stages of IoT botnet detection using monolithic deep learning algorithms that could require more time for training and detection.We,however,propose an edge-based deep learning system for the detection of the early stages of IoT botnets in smart cities.The proposed system,which we call EDIT(Edge-based Detection of early-stage IoT Botnet),aims to detect abnormalities in network communication traffic caused by early-stage IoT botnets based on the modular neural network(MNN)method at multi-access edge computing(MEC)servers.MNN can improve detection accuracy and efficiency by leveraging parallel computing on MEC.According to the findings,EDIT has a lower false-negative rate compared to a monolithic approach and other studies.At the MEC server,EDIT takes as little as 16 ms for the detection of an IoT botnet.
基金the financial support from the National Natural Science Foundation of China(62021003,61890930-5,61903012,62073006)Beijing Natural Science Foundation(42130232)the National Key Research and Development Program of China(2021ZD0112301,2021ZD0112302)。
文摘Air pollution control poses a major problem in the implementation of municipal solid waste incineration(MSWI).Accurate prediction of nitrogen oxides(NO_(x))concentration plays an important role in efficient NO_(x)emission controlling.In this study,a modular long short-term memory(M-LSTM)network is developed to design an efficient prediction model for NO_(x)concentration.First,the fuzzy C means(FCM)algorithm is utilized to divide the task into several sub-tasks,aiming to realize the divide-and-conquer ability for complex task.Second,long short-term memory(LSTM)neural networks are applied to tackle corresponding sub-tasks,which can improve the prediction accuracy of the sub-networks.Third,a cooperative decision strategy is designed to guarantee the generalization performance during the testing or application stage.Finally,after being evaluated by a benchmark simulation,the proposed method is applied to a real MSWI process.And the experimental results demonstrate the considerable prediction ability of the M-LSTM network.
基金supported by the National Natural Science Foundation of China(No.12175080)the Fundamental Research Funds for the Central Universities,China(No.CCNU22JC009)。
文摘Time delay and coupling strength are important factors that affect the synchronization of neural networks.In this study,a modular neural network containing subnetworks of different scales was constructed using the Hodgkin–Huxley(HH)neural model;i.e.,a small-scale random network was unidirectionally connected to a large-scale small-world network through chemical synapses.Time delays were found to induce multiple synchronization transitions in the network.An increase in coupling strength also promoted synchronization of the network when the time delay was an integer multiple of the firing period of a single neuron.Considering that time delays at different locations in a modular network may have different effects,we explored the influence of time delays within each subnetwork and between two subnetworks on the synchronization of modular networks.We found that when the subnetworks were well synchronized internally,an increase in the time delay within both subnetworks induced multiple synchronization transitions of their own.In addition,the synchronization state of the small-scale network affected the synchronization of the large-scale network.It was surprising to find that an increase in the time delay between the two subnetworks caused the synchronization factor of the modular network to vary periodically,but it had essentially no effect on the synchronization within the receiving subnetwork.By analyzing the phase difference between the two subnetworks,we found that the mechanism of the periodic variation of the synchronization factor of the modular network was the periodic variation of the phase difference.Finally,the generality of the results was demonstrated by investigating modular networks at different scales.
基金This reasearch was supported by the Science Foundation of Guangxi under grant No.0339025the Natural Sciences Foundation of China under grant No.40075021.
文摘In terms of the modular fuzzy neural network (MFNN) combining fuzzy c-mean (FCM) cluster and single-layer neural network, a short-term climate prediction model is developed. It is found from modeling results that the MFNN model for short-term climate prediction has advantages of simple structure, no hidden layer and stable network parameters because of the assembling of sound functions of the self-adaptive learning, association and fuzzy information processing of fuzzy mathematics and neural network methods. The case computational results of Guangxi flood season (JJA) rainfall show that the mean absolute error (MAE) and mean relative error (MRE) of the prediction during 1998-2002 are 68.8 mm and 9.78%, and in comparison with the regression method, under the conditions of the same predictors and period they are 97.8 mm and 12.28% respectively. Furthermore, it is also found from the stability analysis of the modular model that the change of the prediction results of independent samples with training times in the stably convergent interval of the model is less than 1.3 mm. The obvious oscillation phenomenon of prediction results with training times, such as in the common back-propagation neural network (BPNN) model, does not occur, indicating a better practical application potential of the MFNN model.
基金National key special projects for major scientific instruments and equipment development(2017YFF0107400)。
文摘The magnetic compensation of aeromagnetic survey is an important calibration work,which has a great impact on the accuracy of measurement.In an aeromagnetic survey flight,measurement data consists of diurnal variation,aircraft maneuver interference field,and geomagnetic field.In this paper,appropriate physical features and the modular feedforward neural network(MFNN)with Levenberg-Marquard(LM)back propagation algorithm are adopted to supervised learn fluctuation of measuring signals and separate the interference magnetic field from the measurement data.LM algorithm is a kind of least square estimation algorithm of nonlinear parameters.It iteratively calculates the jacobian matrix of error performance and the adjustment value of gradient with the regularization method.LM algorithm’s computing efficiency is high and fitting error is very low.The fitting performance and the compensation accuracy of LM-MFNN algorithm are proved to be much better than those of TOLLES-LAWSON(T-L)model with the linear least square(LS)solution by fitting experiments with five different aeromagnetic surveys’data.
基金supported by the National Key R&D Program of China(Project No.2022YFB4700100)the Innovation Promotion Research Association of the Chinese Academy of Sciences(NO.2022199)+2 种基金the Applied Basic Research Program of Liaoning Province(NO.2023JH2/101600037)the National Natural Science Foundation of China(Grant No.62403454 and 62303446)the Hong Kong Research Grants Council(NO.11216120).
文摘In vitro neural networks offer a simplified model to study brain nervous system functions and represent a vital platform for investigating cerebral neural activities.Microelectrode array(MEA)chips are commonly used to construct modular neural networks and enable electrical stimulation and recording for uncovering signal generation and conduction mechanisms.However,conventional two-dimensional(2D)MEA chips face significant limitations,including restricted neuronal growth dimensions and insufficient neuron density.Herein,we present a novel micro-integrated chip featuring a three-dimensional(3D)physical microtube array that facilitates the regulated,confined growth of neurons.The microtube array not only provides a 3D microenvironment for neuronal growth and differentiation but also enhances neuronal network density and structural organization.Furthermore,by integrating the microtube array with a customized MEA,precise electrical stimulation can be applied to modular neural networks.Experimental results demonstrate that electrical stimulation effectively promotes the formation of connection pathways between adjacent 3D neural networks.Variable-parameter electrical stimulation experiments reveal that increasing voltage enhances the Young’s modulus of neurons,highlighting the method’s role in supporting the stable development of neuronal networks.This modular culture platform,combined with precise electrical stimulation,paves the way for constructing high-density 3D neuronal networks and enables synchronous control of modular neural activities.The proposed approach holds significant potential for advancing applications in neuroscience,tissue engineering,and organ-onchip technologies.
