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A new quantum key distribution resource allocation and routing optimization scheme

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Chinese Physics B 2024年第3期33卷 244-259页

作者：毕琳袁晓同吴炜杰林升熙 School of Computer Science and Technology Changchun University of Science and TechnologyChangchun 130012China key laboratory of network and Information security in Jilin province Changchun 130012China

Quantum key distribution(QKD)is a technology that can resist the threat of quantum computers to existing conventional cryptographic protocols.However,due to the stringent requirements of the quantum key generation environment,the generated quantum keys are considered valuable,and the slow key generation rate conflicts with the high-speed data transmission in traditional optical networks.In this paper,for the QKD network with a trusted relay,which is mainly based on point-to-point quantum keys and has complex changes in network resources,we aim to allocate resources reasonably for data packet distribution.Firstly,we formulate a linear programming constraint model for the key resource allocation(KRA)problem based on the time-slot scheduling.Secondly,we propose a new scheduling scheme based on the graded key security requirements(GKSR)and a new micro-log key storage algorithm for effective storage and management of key resources.Finally,we propose a key resource consumption(KRC)routing optimization algorithm to properly allocate time slots,routes,and key resources.Simulation results show that the proposed scheme significantly improves the key distribution success rate and key resource utilization rate,among others.

关键词： quantum key distribution(QKD) resource allocation key storage routing algorithm

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A New Encrypted Traffic Identification Model Based on VAE-LSTM-DRN

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Computers, Materials & Continua 2024年第1期78卷 569-588页

作者： Haizhen Wang Jinying Yan Na Jia College of Computer and Control Engineering Qiqihar UniversityQiqihar161006China Heilongjiang key laboratory of Big data network security Detection and Analysis Qiqihar UniversityQiqiharChina

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.

关键词： data enhancement LSTM deep residual network VAE

Big data Access Control Mechanism Based on Two-Layer Permission Decision Structure

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Computers, Materials & Continua 2024年第4期79卷 1705-1726页

作者： Aodi Liu Na Wang Xuehui Du Dibin Shan Xiangyu Wu Wenjuan Wang He’nan province key laboratory of Information security Information Engineering UniversityZhengzhou450000China

Big data resources are characterized by large scale, wide sources, and strong dynamics. Existing access controlmechanisms based on manual policy formulation by security experts suffer from drawbacks such as low policymanagement efficiency and difficulty in accurately describing the access control policy. To overcome theseproblems, this paper proposes a big data access control mechanism based on a two-layer permission decisionstructure. This mechanism extends the attribute-based access control (ABAC) model. Business attributes areintroduced in the ABAC model as business constraints between entities. The proposed mechanism implementsa two-layer permission decision structure composed of the inherent attributes of access control entities and thebusiness attributes, which constitute the general permission decision algorithm based on logical calculation andthe business permission decision algorithm based on a bi-directional long short-term memory (BiLSTM) neuralnetwork, respectively. The general permission decision algorithm is used to implement accurate policy decisions,while the business permission decision algorithm implements fuzzy decisions based on the business constraints.The BiLSTM neural network is used to calculate the similarity of the business attributes to realize intelligent,adaptive, and efficient access control permission decisions. Through the two-layer permission decision structure,the complex and diverse big data access control management requirements can be satisfied by considering thesecurity and availability of resources. Experimental results show that the proposed mechanism is effective andreliable. In summary, it can efficiently support the secure sharing of big data resources.

关键词： Big data access control data security BiLSTM

Artificial Immune Detection for network Intrusion data Based on Quantitative Matching Method

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Computers, Materials & Continua 2024年第2期78卷 2361-2389页

作者： CaiMing Liu Yan Zhang Zhihui Hu Chunming Xie School of Electronic Information and Artificial Intelligence Leshan Normal UniversityLeshan614000China Intelligent network security Detection and Evaluation laboratory Leshan Normal UniversityLeshan614000China Internet Natural Language Intelligent Processing key laboratory of Education Department of sichuan province Leshan Normal UniversityLeshan614000China

Artificial immune detection can be used to detect network intrusions in an adaptive approach and proper matching methods can improve the accuracy of immune detection methods.This paper proposes an artificial immune detection model for network intrusion data based on a quantitative matching method.The proposed model defines the detection process by using network data and decimal values to express features and artificial immune mechanisms are simulated to define immune elements.Then,to improve the accuracy of similarity calculation,a quantitative matching method is proposed.The model uses mathematical methods to train and evolve immune elements,increasing the diversity of immune recognition and allowing for the successful detection of unknown intrusions.The proposed model’s objective is to accurately identify known intrusions and expand the identification of unknown intrusions through signature detection and immune detection,overcoming the disadvantages of traditional methods.The experiment results show that the proposed model can detect intrusions effectively.It has a detection rate of more than 99.6%on average and a false alarm rate of 0.0264%.It outperforms existing immune intrusion detection methods in terms of comprehensive detection performance.

关键词： Immune detection network intrusion network data signature detection quantitative matching method

Enhanced Privacy-Preserving WiFi Fingerprint Localization from CL Encryption

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Chinese Journal of Electronics 2024年

作者： Zhiwei WANG Qiuchi ZHU Zhenqi ZHANG School of Computer Nanjing University of Posts and Telecommunications State key laboratory of Public Big data Guizhou University Advanced Cryptography and System security key laboratory of sichuan province Chengdu University of Information Engineering key laboratory of Computational Science and Application of Hainan province Hainan Normal University

The WiFi fingerprint-based localization method is considered one of the most popular techniques for indoor localization. In INFOCOM'14, Li et al. proposed a WiFi fingerprint localization system based on Paillier encryption, which is claimed to protect both client C' s location privacy and service provider S's database privacy.However, Yang et al. presented a practical data privacy attack in INFOCOM'18, which allows a polynomial time attacker to obtain S's database. We propose a novel WiFi fingerprint localization system based on CL(CastagnosLaguillaumie) encryption, which has a trustless setup and is efficient due to the excellent properties of CL encryption.To prevent Yang et al.'s attack, the system requires that S selects only the locations from its database that can receive the nonzero signals from all the available APs in C ' s nonzero fingerprint in order to determine C 's location. security analysis shows that our scheme is secure under Li et al.'s threat model. Furthermore, to enhance the security level of PriWFLCL, we propose a secure and efficient zero-knowledge proof protocol for the discrete logarithm relations in C 's encrypted localization queries.

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An Enhanced Clustering-Based(k, t)-Anonymity Algorithm for Graphs

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Chinese Journal of Electronics 2024年

作者： Yuanyuan WANG Xing ZHANG Zhiguang CHU Wei SHI Xiang LI School of Electronics and Information Engineering Liaoning University of Technology key laboratory of security for network and data in Industrial Internet of Liaoning province Faculty of Information Technology Beijing University of Technology

As people become increasingly reliant on the Internet, securely storing and publishing private data has become an important issue. In real life, the release of graph data can lead to privacy breaches, which is a highly challenging problem. Although current research has addressed the issue of identity disclosure, there are still two challenges: First, the privacy protection for large-scale datasets is not yet comprehensive; Second, it is difficult to simultaneously protect the privacy of nodes, edges, and attributes in social networks. To address these issues, this paper proposes a(k, t)-graph anonymity algorithm based on enhanced clustering. The algorithm uses k-means++ clustering for k-anonymity and t-closeness to improve k-anonymity. We evaluated the privacy and efficiency of this method on two datasets and achieved good results. This research is of great significance for addressing the problem of privacy breaches that may arise from the publication of graph data.

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Epileptic brain network mechanisms and neuroimaging techniques for the brain network

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Neural Regeneration Research 2024年第12期19卷 2637-2648页

作者： Yi Guo Zhonghua Lin Zhen Fan Xin Tian Department of Neurology Sichuan Provincial People’s HospitalUniversity of Electronic Science and Technology of ChinaChengduSichuan ProvinceChina sichuan Provincial Center for Mental Health Sichuan Provincial People’s HospitalUniversity of Electronic Science and Technology of ChinaChengduSichuan ProvinceChina Department of Geriatrics Sichuan Provincial People’s HospitalUniversity of Electronic Science and Technology of ChinaChengduSichuan ProvinceChina Department of Neurology Chongqing Key Laboratory of NeurologyThe First Affiliated Hospital of Chongqing Medical UniversityChongqingChina

Epilepsy can be defined as a dysfunction of the brain network,and each type of epilepsy involves different brain-network changes that are implicated diffe rently in the control and propagation of interictal or ictal discharges.Gaining more detailed information on brain network alterations can help us to further understand the mechanisms of epilepsy and pave the way for brain network-based precise therapeutic approaches in clinical practice.An increasing number of advanced neuroimaging techniques and electrophysiological techniques such as diffusion tensor imaging-based fiber tra ctography,diffusion kurtosis imaging-based fiber tractography,fiber ball imagingbased tra ctography,electroencephalography,functional magnetic resonance imaging,magnetoencephalography,positron emission tomography,molecular imaging,and functional ultrasound imaging have been extensively used to delineate epileptic networks.In this review,we summarize the relevant neuroimaging and neuroelectrophysiological techniques for assessing structural and functional brain networks in patients with epilepsy,and extensively analyze the imaging mechanisms,advantages,limitations,and clinical application ranges of each technique.A greater focus on emerging advanced technologies,new data analysis software,a combination of multiple techniques,and the construction of personalized virtual epilepsy models can provide a theoretical basis to better understand the brain network mechanisms of epilepsy and make surgical decisions.

关键词： electrophysiological techniques epilepsy functional brain network functional magnetic resonance imaging functional near-infrared spectroscopy machine leaning molecular imaging neuroimaging techniques structural brain network virtual epileptic models

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Method and Practice of Trusted Embedded Computing and data Transmission Protection Architecture Based on Android

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Chinese Journal of Electronics 2024年第3期33卷 623-634页

作者： Yichuan WANG Wen GAO Xinhong HEI Yanning DU School of Computer Science and Engineering Xi'an University of Technology Shaanxi key laboratory for network Computing and security Technology

In recent years,the rapid development of Internet technology has constantly enriched people's daily life and gradually changed from the traditional computer terminal to the mobile terminal.But with it comes the security problems brought by the mobile terminal.Especially for Android system,due to its open source nature,malicious applications continue to emerge,which greatly threatens the data security of users.Therefore,this paper proposes a method of trusted embedded static measurement and data transmission protection architecture based on Android to reduce the risk of data leakage in the process of terminal storage and transmission.We conducted detailed data and feasibility analysis of the proposed method from the aspects of time consumption,storage overhead and security.The experimental results show that this method can detect Android system layer attacks such as self-booting of the malicious module and improve the security of data encryption and transmission process effectively.Compared with the native system,the additional performance overhead is small.

关键词： Embedded computing Protocols data security Computer architecture Encryption Internet data communication

An Efficient and Provably Secure SM2 key-Insulated Signature Scheme for Industrial Internet of Things

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工程与科学中的计算机建模(英文) 2024年第1期138卷 903-915页

作者： Senshan Ouyang Xiang Liu Lei Liu Shangchao Wang Baichuan Shao Yang Zhao School of Mechanical Engineering Northwestern Polytechnical UniversityXi’anChina Department of Process and Information Technology Chengdu Aircraft Industrial(Group)Co.Ltd.ChengduChina School of Information and Software Engineering The Network and Data Security Key Laboratory of Sichuan ProvinceUniversity of Electronic Science and Technology of ChinaChengduChina

With the continuous expansion of the Industrial Internet of Things(IIoT),more andmore organisations are placing large amounts of data in the cloud to reduce overheads.However,the channel between cloud servers and smart equipment is not trustworthy,so the issue of data authenticity needs to be addressed.The SM2 digital signature algorithm can provide an authentication mechanism for data to solve such problems.Unfortunately,it still suffers from the problem of key exposure.In order to address this concern,this study first introduces a key-insulated scheme,SM2-KI-SIGN,based on the SM2 algorithm.This scheme boasts strong key insulation and secure keyupdates.Our scheme uses the elliptic curve algorithm,which is not only more efficient but also more suitable for IIoT-cloud environments.Finally,the security proof of SM2-KI-SIGN is given under the Elliptic Curve Discrete Logarithm(ECDL)assumption in the random oracle.

关键词： key-insulated SM2 algorithm digital signature Industrial Internet of Things(IIoT) provable security