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Dynamical behaviors in discrete memristor-coupled small-world neuronal networks

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Chinese Physics B 2024年第4期33卷 729-734页

作者：鲁婕妤谢小华卢亚平吴亚联李春来马铭磷 school of Automation and Electronic Information Xiangtan UniversityXiangtan 411105China school of Computer science school of cyberspace science Xiangtan UniversityXiangtan 411105China

The brain is a complex network system in which a large number of neurons are widely connected to each other and transmit signals to each *** memory characteristic of memristors makes them suitable for simulating neuronal synapses with *** this paper,a memristor is used to simulate a synapse,a discrete small-world neuronal network is constructed based on Rulkov neurons and its dynamical behavior is *** explore the influence of system parameters on the dynamical behaviors of the discrete small-world network,and the system shows a variety of firing patterns such as spiking firing and triangular burst firing when the neuronal parameterαis *** results of a numerical simulation based on Matlab show that the network topology can affect the synchronous firing behavior of the neuronal network,and the higher the reconnection probability and number of the nearest neurons,the more significant the synchronization state of the *** addition,by increasing the coupling strength of memristor synapses,synchronization performance is *** results of this paper can boost research into complex neuronal networks coupled with memristor synapses and further promote the development of neuroscience.

关键词： small-world networks Rulkov neurons memristor synchronization

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Fusion of Spiral Convolution-LSTM for Intrusion Detection Modeling

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Computers, Materials & Continua 2024年第5期79卷 2315-2329页

作者： Fei Wang Zhen Dong school of cyberspace Security Gansu University of Political Science and LawLanzhou73000China

Aiming at the problems of low accuracy and slow convergence speed of current intrusion detection models,SpiralConvolution is combined with Long Short-Term Memory Network to construct a new intrusion detection *** dataset is first preprocessed using solo thermal encoding and normalization *** the spiral convolution-Long Short-Term Memory Network model is constructed,which consists of spiral convolution,a two-layer long short-term memory network,and a *** is shown through experiments that the model is characterized by high accuracy,small model computation,and fast convergence speed relative to previous deep learning *** model uses a new neural network to achieve fast and accurate network traffic intrusion *** model in this paper achieves 0.9706 and 0.8432 accuracy rates on the NSL-KDD dataset and the UNSWNB-15 dataset under five classifications and ten classes,respectively.

关键词： Intrusion detection deep learning spiral convolution long and short term memory networks 1D-spiral convolution

Adaptive Graph Convolutional Adjacency Matrix Network for Video Summarization

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Computers, Materials & Continua 2024年第8期80卷 1947-1965页

作者： Jing Zhang Guangli Wu Shanshan Song school of cyberspace Security Gansu University of Political Science and LawLanzhou730000China

Video summarization aims to select key frames or key shots to create summaries for fast retrieval,compression,and efficient browsing of *** neural networks efficiently capture information about graph nodes and their neighbors,but ignore the dynamic dependencies between *** address this challenge,we propose an innovative Adaptive Graph Convolutional Adjacency Matrix Network(TAMGCN),leveraging the attention mechanism to dynamically adjust dependencies between graph ***,we first segment shots and extract features of each frame,then compute the representative features of each ***,we utilize the attention mechanism to dynamically adjust the adjacency matrix of the graph convolutional network to better capture the dynamic dependencies between graph ***,we fuse temporal features extracted by Bi-directional Long Short-Term Memory network with structural features extracted by the graph convolutional network to generate high-quality *** experiments are conducted on two benchmark datasets,TVSum and SumMe,yielding F1-scores of 60.8%and 53.2%,*** results demonstrate that our method outperforms most state-of-the-art video summarization techniques.

关键词： Attention mechanism deep learning graph neural network key-shot video summarization

Authenticated Digital Currency Redaction with Stronger Privacy and Usability

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China Communications 2024年第6期21卷 219-236页

作者： Tang Yongli Li Ying Zhao Zongqu Li Yuanhong Guo Rui school of Computer science and technology Henan Polytechnic UniversityJiaozuo 454000China school of cyberspace Security Xi’an University of Posts and TelecommunicationsXi’an 710121China

With the promotion of digital currency,how to effectively solve the authenticity,privacy and usability of digital currency issuance has been a key *** signature scheme(RSS)can provide the verification of the integrity and source of the generated sub-documents and solve the privacy problem in digital currency by removing blocks from the signed ***,it has not realized the consolidation of signed documents,which can not solve the problem of merging two digital ***,we introduce the concept of weight based on the threshold secret sharing scheme(TSSS)and present a redactable signature scheme with merge algorithm(RSS-MA)using the quasi-commutative *** scheme can reduce the communication overhead by utilizing the merge algorithm when transmitting multiple digital currency ***,this can effectively hide the scale of users’private monetary assets and the number of transactions between *** meeting the three properties of digital currency issuance,in order to ensure the availability of digital currency after redacting,editors shall not remove the relevant identification information block form digital ***,our security proof and the analysis of efficiency show that RSS-MA greatly improves the communication and computation efficiency when transmitting multiple signatures.

关键词： authenticity verification digital currency privacy protection RSS-MA TSSS usability

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Dynamics and synchronization in a memristor-coupled discrete heterogeneous neuron network considering noise

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Chinese Physics B 2024年第2期33卷 537-544页

作者：晏询李志军李春来 school of Automation and Electronic Information Xiangtan UniversityXiangtan 411105China school of Computer science&school of cyberspace science Xiangtan UniversityXiangtan 411105China

Research on discrete memristor-based neural networks has received much ***,current research mainly focuses on memristor–based discrete homogeneous neuron networks,while memristor-coupled discrete heterogeneous neuron networks are rarely *** this study,a new four-stable discrete locally active memristor is proposed and its nonvolatile and locally active properties are verified by its power-off plot and DC V–I *** on two-dimensional(2D)discrete Izhikevich neuron and 2D discrete Chialvo neuron,a heterogeneous discrete neuron network is constructed by using the proposed discrete memristor as a coupling synapse connecting the two heterogeneous *** the coupling strength as the control parameter,chaotic firing,periodic firing,and hyperchaotic firing patterns are *** particular,multiple coexisting firing patterns are observed,which are induced by different initial values of the *** synchronization between the two heterogeneous neurons is discussed and it is found that they can achieve perfect synchronous at large coupling ***,the effect of Gaussian white noise on synchronization behaviors is also *** demonstrate that the presence of noise not only leads to the transition of firing patterns,but also achieves the phase synchronization between two heterogeneous neurons under low coupling strength.

关键词： heterogeneous neuron network discrete memristor coexisting attractors synchronization noise

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A Time Series Intrusion Detection Method Based on SSAE,TCN and Bi-LSTM

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Computers, Materials & Continua 2024年第1期78卷 845-871页

作者： Zhenxiang He Xunxi Wang Chunwei Li school of cyberspace Security Gansu University of Political Science and LawLanzhou730000China

In the fast-evolving landscape of digital networks,the incidence of network intrusions has escalated ***,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 *** leads to compromised detection accuracy and overlooked temporal *** this gap,we introduce a novel SSAE-TCN-BiLSTM(STL)model that integrates time series analysis,significantly enhancing detection *** 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 *** 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 *** 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 *** 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 *** 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.

关键词： Network intrusion detection bidirectional long short-term memory network time series stacked sparse autoencoder temporal convolutional network time steps

Deep Learning Social Network Access Control Model Based on User Preferences

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Computer Modeling in Engineering & sciences 2024年第7期140卷 1029-1044页

作者： Fangfang Shan Fuyang Li Zhenyu Wang Peiyu Ji Mengyi Wang Huifang Sun school of Computer science Zhongyuan University of TechnologyZhengzhou450007China Henan Key Laboratory of cyberspace Situation Awareness Zhengzhou450001China

A deep learning access controlmodel based on user preferences is proposed to address the issue of personal privacy leakage in social ***,socialusers andsocialdata entities are extractedfromthe social networkandused to construct homogeneous and heterogeneous ***,a graph neural networkmodel is designed based on user daily social behavior and daily social data to simulate the dissemination and changes of user social preferences and user personal preferences in the social ***,high-order neighbor nodes,hidden neighbor nodes,displayed neighbor nodes,and social data nodes are used to update user nodes to expand the depth and breadth of user ***,a multi-layer attention network is used to classify user nodes in the homogeneous graph into two classes:allow access and deny *** fine-grained access control problem in social networks is transformed into a node classification problem in a graph neural *** model is validated using a dataset and compared with other methods without losing *** model improved accuracy by 2.18%compared to the baseline method GraphSAGE,and improved F1 score by 1.45%compared to the baseline method,verifying the effectiveness of the model.

关键词： Graph neural networks user preferences access control social network

FMSA:a meta-learning framework-based fast model stealing attack technique against intelligent network intrusion detection systems

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Cybersecurity 2024年第1期7卷 110-121页

作者： Kaisheng Fan Weizhe Zhang Guangrui Liu Hui He school of cyberspace science Harbin Institute of TechnologyHarbin 150001China Department of New Networks Peng Cheng LaboratoryShenzhen 518055GuangdongChina

Intrusion detection systems are increasingly using machine *** machine learning has shown excellent performance in identifying malicious traffic,it may increase the risk of privacy *** paper focuses on imple-menting a model stealing attack on intrusion detection *** model stealing attacks are hard to imple-ment in practical network environments,as they either need private data of the victim dataset or frequent access to the victim *** this paper,we propose a novel solution called Fast Model Stealing Attack(FMSA)to address the problem in the field of model stealing *** also highlight the risks of using ML-NIDS in network ***,meta-learning frameworks are introduced into the model stealing algorithm to clone the victim model in a black-box ***,the number of accesses to the target model is used as an optimization term,resulting in minimal queries to achieve model ***,adversarial training is used to simulate the data distribution of the target model and achieve the recovery of privacy *** experiments on multiple public datasets,compared to existing state-of-the-art algorithms,FMSA reduces the number of accesses to the target model and improves the accuracy of the clone model on the test dataset to 88.9%and the similarity with the target model to 90.1%.We can demonstrate the successful execution of model stealing attacks on the ML-NIDS system even with protective measures in place to limit the number of anomalous queries.

关键词： AI security Model stealing attack Network intrusion detection Meta learning

Lightweight Steganography Detection Method Based on Multiple Residual Structures and Transformer

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Chinese Journal of Electronics 2024年第4期33卷 965-978页

作者： Hao LI Yi ZHANG Jinwei WANG Weiming ZHANG Xiangyang LUO Key Laboratory of cyberspace Situation Awareness of Henan Province Nanjing University of Information science & technology University of science and technology of China

Existing deep learning-based steganography detection methods utilize convolution to automatically capture and learn steganographic features, yielding higher detection efficiency compared to manually designed steganography detection methods. Detection methods based on convolutional neural network frameworks can extract global features by increasing the network's depth and width. These frameworks are not highly sensitive to global features and can lead to significant resource consumption. This manuscript proposes a lightweight steganography detection method based on multiple residual structures and Transformer(Res Former). A multi-residuals block based on channel rearrangement is designed in the preprocessing layer. Multiple residuals are used to enrich the residual features and channel shuffle is used to enhance the feature representation capability. A lightweight convolutional and Transformer feature extraction backbone is constructed, which reduces the computational and parameter complexity of the network by employing depth-wise separable convolutions. This backbone integrates local and global image features through the fusion of convolutional layers and Transformer, enhancing the network's ability to learn global features and effectively enriching feature diversity. An effective weighted loss function is introduced for learning both local and global features, Bias Loss loss function is used to give full play to the role of feature diversity in classification, and cross-entropy loss function and contrast loss function are organically combined to enhance the expression ability of features. Based on Boss Base-1.01, BOWS2 and ALASKA#2, extensive experiments are conducted on the stego images generated by spatial and JPEG domain adaptive steganographic algorithms, employing both classical and state-of-theart steganalysis techniques. The experimental results demonstrate that compared to the SRM, SRNet, Sia Steg Net,CSANet, LWENet, and Sia IRNet methods, the proposed Res

关键词： Q-factor Steganography Accuracy Convolution Computational modeling Transform coding Transformers