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Application of meta-learning in cyberspace security:a survey

Digital Communications and Networks 2023年第1期9卷 67-78页

作者： Aimin Yang Chaomeng Lu Jie Li Xiangdong Huang Tianhao Ji Xichang Li Yichao Sheng Hebei key laboratory of Data Science and Application North China University of Science and TechnologyTangshan063000China Hebei engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes North China University of Science and TechnologyTangshan063000China The Key Laboratory of Engineering Computing in Tangshan City North China University of Science and TechnologyTangshan063000China

In recent years,machine learning has made great progress in intrusion detection,network protection,anomaly detection,and other issues in cyberspace.However,these traditional machine learning algorithms usually require a lot of data to learn and have a low recognition rate for unknown attacks.Among them,“one-shot learning”,“few-shot learning”,and“zero-shot learning”are challenges that cannot be ignored for traditional machine learning.The more intractable problem in cyberspace security is the changeable attack mode.When a new attack mode appears,there are few or even zero samples that can be learned.Meta-learning comes from imitating human problem-solving methods as humans can quickly learn unknown things based on their existing knowledge when learning.Its purpose is to quickly obtain a model with high accuracy and strong generalization through less data training.This article first divides the meta-learning model into five research directions based on different principles of use.They are model-based,metric-based,optimization-based,online-learning-based,or stacked ensemble-based.Then,the current problems in the field of cyberspace security are categorized into three branches:cyber security,information security,and artificial intelligence security according to different perspectives.Then,the application research results of various meta-learning models on these three branches are reviewed.At the same time,based on the characteristics of strong generalization,evolution,and scalability of meta-learning,we contrast and summarize its advantages in solving problems.Finally,the prospect of future deep application of meta-learning in the field of cyberspace security is summarized.

关键词： Meta-learning Cyberspace security Machine learning Few-shot learning

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Set pair three-way overlapping community discovery algorithm for weighted social internet of things

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Digital Communications and Networks 2023年第1期9卷 3-13页

作者： Chunying Zhang Jing Ren Lu Liu Shouyue Liu Xiaoqi Li Liya Wang College of Science North China University of Science and TechnologyTangshanHebeiChina Hebei engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes North China University of Science and TechnologyTangshanHebeiChina Hebei key laboratory of Data Science and Application North China University of Science and TechnologyTangshanHebeiChina The Key Laboratory of Engineering Computing in Tangshan City North China University of Science and TechnologyTangshanHebeiChina tangshan Intelligent Industry and Image Processing Technology Innovation Center North China University of Science and TechnologyTangshanHebeiChina

There are many problems in Social Internet of Things(IoTs),such as complex topology information,different degree of association between nodes and overlapping communities.The idea of set pair information grain computing and clustering is introduced to solve the above problems so as to accurately describe the similarity between nodes and fully explore the multi-community structure.A Set Pair Three-Way Overlapping Community Discovery Algorithm for Weighted Social Internet of Things(WSIoT-SPTOCD)is proposed.In the local network structure,which fully considers the topological information between nodes,the set pair connection degree is used to analyze the identity,difference and reverse of neighbor nodes.The similarity degree of different neighbor nodes is defined from network edge weight and node degree,and the similarity measurement method of set pair between nodes based on the local information structure is proposed.According to the number of nodes'neighbors and the connection degree of adjacent edges,the clustering intensity of nodes is defined,and an improved algorithm for initial value selection of k-means is proposed.The nodes are allocated according to the set pair similarity between nodes and different communities.Three-way community structures composed of a positive domain,boundary domain and negative domain are generated iteratively.Next,the overlapping node set is generated according to the calculation results of community node membership.Finally,experiments are carried out on artificial networks and real networks.The results show that WSIoT-SPTOCD performs well in terms of standardized mutual information,overlapping community modularity and F1.

关键词： Social internet of things Set pair analysis K-means Local information structure Overlapping community

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Review on application progress of federated learning model and security hazard protection

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Digital Communications and Networks 2023年第1期9卷 146-158页

作者： Aimin Yang Zezhong Ma Chunying Zhang Yang Han Zhibin Hu Wei Zhang Xiangdong Huang Yafeng Wu Hebei engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes North China University of Science and TechnologyTangshanHebeiChina Hebei key laboratory of Data Science and Application North China University of Science and TechnologyTangshanHebeiChina The Key Laboratory of Engineering Computing in Tangshan City North China University of Science and TechnologyTangshanHebeiChina tangshan Intelligent Industry and Image Processing Technology Innovation Center North China University of Science and TechnologyTangshanHebeiChina College of Science North China University of Science and TechnologyTangshanHebeiChina College of Artificial Intelligence North China University of Science and TechnologyTangshanHebeiChina

Federated learning is a new type of distributed learning framework that allows multiple participants to share training results without revealing their data privacy.As data privacy becomes more important,it becomes difficult to collect data from multiple data owners to make machine learning predictions due to the lack of data security.Data is forced to be stored independently between companies,creating“data silos”.With the goal of safeguarding data privacy and security,the federated learning framework greatly expands the amount of training data,effectively improving the shortcomings of traditional machine learning and deep learning,and bringing AI algorithms closer to our reality.In the context of the current international data security issues,federated learning is developing rapidly and has gradually moved from the theoretical to the applied level.The paper first introduces the federated learning framework,analyzes its advantages,reviews the results of federated learning applications in industries such as communication and healthcare,then analyzes the pitfalls of federated learning and discusses the security issues that should be considered in applications,and finally looks into the future of federated learning and the application layer.

关键词： Data silos Machine learning Federated learning Privacy protection Learning framework

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WGDI:A user-friendly toolkit for evolutionary analyses of whole-genome duplications and ancestral karyotypes

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Molecular Plant 2022年第12期15卷 1841-1851页

作者： Pengchuan Sun Beibei Jiao Yongzhi Yang Lanxing Shan Ting Li Xiaonan Li Zhenxiang Xi Xiyin Wang Jianquan Liu key laboratory of Bio-Resource and Eco-Environment of Ministry of Education and State key laboratory of Hydraulics and Mountain River engineering College of Life SciencesSichuan UniversityChengduChina Center for Genomics and Bio-computing North China University of Science and TechnologyTangshanHebeiChina State key laboratory of Grassland Agro-Ecosystem College of EcologyLanzhou UniversityLanzhouChina

Evidence of whole-genome duplications(WGDs)and subsequent karyotype changes has been detected in most major lineages of living organisms on Earth.To clarify the complex resulting multi-layered patterns of gene collinearity in genome analyses,there is a need for convenient and accurate toolkits.To meet this need,we developed WGDI(Whole-Genome Duplication Integrated analysis),a Python-based command-line tool that facilitates comprehensive analysis of recursive polyploidization events and cross-species genome alignments.WGDI supports three main workflows(polyploid inference,hierarchical inference of genomic homology,and ancestral chromosome karyotyping)that can improve the detection of WGD and characterization of WGD-related events based on high-quality chromosome-level genomes.Significantly,it can extract complete synteny blocks and facilitate reconstruction of detailed karyotype evolution.This toolkit is freely available at GitHub(https://***/SunPengChuan/wgdi).As an example of its application,WGDI convincingly clarified karyotype evolution in Aquilegia coerulea and Vitis vinifera following WGDs and rejected the hypothesis that Aquilegia contributed as a parental lineage to the allopolyploid origin of core dicots.

关键词： WGDI polyploidy collinearity inference of hierarchical and event-related gene collinearity ancestral chromosome karyotype

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Enhanced afterglow performance of CaAl_(2)O_(4):Eu^(2+),Nd^(3+) phosphors by co-doping Gd^(3+)

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Journal of Rare Earths 2021年第8期39卷 930-937页

作者： Mingxi Zhang Fengfeng Li Yongcheng Lin Yan Li Yi Shen Light Alloy Research Institute of Central South University Changsha 410083China key laboratory of Inorganic Nonmetallic Materials Hebei Province Key Laboratory of Environment Functional Materials of Tangshan CityCollege of Materials Science and EngineeringNorth China University of Science and TechnologyTangshan 063210China School of Mechanical and Electrical engineering.Central South University Changsha 410083China

In order to effectively improve the afterglow properties of CaAl_(2)O_(4):Eu^(2+),Nd^(3+) phosphors,a series of Ca_(0.982-x)Al_(2)O_(4):0.012 Eu^(2+),0.006 Nd^(3+),xGd^(3+)(x=0,0.012,0.024,0.036,0.048,0.060 mol) phosphors were prepared by a high-temperature solid-phase approach.Crystalline composition and microstructure were characterized by XRD,TEM,HRTEM,and XPS,luminescence properties were systematically analyzed by fluorescence spectra,afterglow decay curves and TL glow curve.Results show that all of Ca_(0.982-x)Al_(2)O_(4):0.012 Eu^(2+),0.006 Nd^(3+),xGd^(3+)phosphors belong to monoclinic CaAl_(2)O_(4),without other cystalline phase.The blue emission at 442 nm is observed,which is assigned to the 4f^(6)5 d→4f^(7) transition of Eu^(2+) ions.Doping with appropriate amount of Gd^(3+) ions(x=0.036 mol) significantly improves the afterglow properties of phosphors,but the excessive doping of Gd^(3+) induces the fluorescent quenching.The doping of moderate Gd^(3+) changes the traps states,the trap depth varies from 0.598 to 0.644 eV and the trap concentration is also greatly improved,thus significantly improving afterglow performance.

关键词： CaAl_(2)O_(4):Eu^(2+),Nd^(3+) Gd^(3+)ions Afterlow Trap depth Mechanism Rare earths

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Large margin deep embedding for aesthetic image classification

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Science China(Information Sciences) 2020年第1期63卷 229-231+259-264页

作者： Guanjun GUO Hanzi WANG Yan YAN Liming ZHANG Bo LI Fujian key laboratory of Sensing and computing for Smart city School of Information Science and EngineeringXiamen University Faculty of Science and Technology University of Macau Beijing key laboratory of Digital Media School of Computer Science and EngineeringBeihang University

Dear editor,The problem of aesthetic image classification has attracted much attention during the past few years. The recently proposed methods [1–4] based on the deep convolutional neural network (CNN)[5,6]have achi... 详细信息

关键词： net CNN image Large margin deep embedding for aesthetic image classification

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SPIDER:Speeding up Side-Channel Vulnerability Detection via Test Suite Reduction

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Tsinghua Science and Technology 2023年第1期28卷 47-58页

作者： Fei Yan Rushan Wu Liqiang Zhang Yue Cao key laboratory of Aerospace Information Security and Trusted computing Ministry of EducationSchool of Cyber Science and EngineeringWuhan UniversityWuhan 430072China

Side-channel attacks allow adversaries to infer sensitive information,such as cryptographic keys or private user data,by monitoring unintentional information leaks of running programs.Prior side-channel detection methods can identify numerous potential vulnerabilities in cryptographic implementations with a small amount of execution traces due to the high diffusion of secret inputs in crypto primitives.However,because non-cryptographic programs cover different paths under various sensitive inputs,extending existing tools for identifying information leaks to non-cryptographic applications suffers from either insufficient path coverage or redundant testing.To address these limitations,we propose a new dynamic analysis framework named SPIDER that uses fuzzing,execution profiling,and clustering for a high path coverage and test suite reduction,and then speeds up the dynamic analysis of side-channel vulnerability detection in non-cryptographic programs.We analyze eight non-cryptographic programs and ten cryptographic algorithms under SPIDER in a fully automated way,and our results confirm the effectiveness of test suite reduction and the vulnerability detection accuracy of the whole framework.

关键词： side-channel detection test suite reduction dynamic analysis

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Tunable Supercontinuum Generated in a Yb^3＋-Doped Microstructure Fiber Pumped by Ti：Sapphire Femtosecond Laser

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Chinese Physics Letters 2018年第10期35卷 31-35页

作者：王伟孟凡超卿源邱石董婷婷朱维震左玉婷韩颖王超齐跃峰侯蓝田 School of Information Science and engineering Yanshan University Qinhuangdao 066004 Jiangsu key laboratory of Meteorological Observation and Information Processing Nanjing University of Information Science and Technology Nanjing 210000 key laboratory for Special Fiber and Fiber Sensor of Hebei Province Qinhuangdao 066004 Intelligence and Information engineering college Tangshan University Tangshan 063000

We experimentally demonstrate that a tunable supercontinuum（SC） can be generated in a Yb3＋-doped microstructure fiber by the concept of wavelength conversion with a Ti：sapphire femtosecond（fs） laser as the pump.Experimental results show that an emission light around 1040 nm in an anomalous dispersion region is first generated and amplified by fs pulses in the normal dispersion region. Then, SC spectra from 1100 to 1380 nm and 630 to 840 nm can be achieved by combined effects of higher-order soliton fission and Raman soliton self-frequency shift in the anomalous dispersion region and self-phase modulation, dispersive wave, and four-wave mixing in the normal dispersion region. It is also demonstrated that the 20 nm change of pump results in a 280 nm broadband shift of soliton and the further red-shift of soliton is limited by OH-absorption at 1380 nm.

关键词： Tunable Supercontinuum Generated in a Yb length MSF SC Doped Microstructure Fiber Pumped by Ti:Sapphire Femtosecond Laser Ti

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Understanding urban structures and crowd dynamics leveraging large-scale vehicle mobility data

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Frontiers of Computer Science 2020年第5期14卷 77-88页

作者： Zhihan Jiang Yan Liu Xiaoliang Fan Cheng Wang Jonathan Li Longbiao Chen Fujian key laboratory of Sensing and computing for Smart Cities School of Information Science and EngineeringXiamen UniversityXiamen361005China

A comprehensive understanding of city structures and urban dynamics can greatly improve the efficiency and quality of urban planning and management,while the traditional approaches of which,such as manual surveys,usually incur substantial labor and time.In this paper,we propose a data-driven framework to sense urban structures and dynamics from large-scale vehicle mobility data.First,we divide the city into fine-grained grids,and cluster the grids with similar mobility features into structured urban areas with a proposed distance-constrained clustering algorithm(DCCA).Second,we detect irregular mobility traffic patterns in each area leveraging an ARIMA-based anomaly detection algorithm(ADAM),and correlate them to the urban social and emergency events.Finally,we build a visualization system to demonstrate the urban structures and crowd dynamics.We evaluate our framework using real-world datasets collected from Xiamen city,China,and the results show that the proposed framework can sense urban structures and crowd comprehensively and effectively.

关键词： vehicle mobility big data spatial clustering event detection urban computing ubiquitous computing

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Universal quantum computation with qudits

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Science China(Physics,Mechanics & Astronomy) 2014年第9期57卷 1712-1717页

作者： LUO MingXing WANG XiaoJun Information Security and National computing Grid laboratory Southwest Jiaotong University State key laboratory of Information Security (Institute of Information engineering Chinese Academy of Sciences) School of Electronic engineering Dublin City University

Quantum circuit model has been widely explored for various quantum applications such as Shors algorithm and Grovers searching algorithm.Most of previous algorithms are based on the qubit systems.Herein a proposal for a universal circuit is given based on the qudit system,which is larger and can store more information.In order to prove its universality for quantum applications,an explicit set of one-qudit and two-qudit gates is provided for the universal qudit computation.The one-qudit gates are general rotation for each two-dimensional subspace while the two-qudit gates are their controlled extensions.In comparison to previous quantum qudit logical gates,each primitive qudit gate is only dependent on two free parameters and may be easily implemented.In experimental implementation,multilevel ions with the linear ion trap model are used to build the qudit systems and use the coupling of neighbored levels for qudit gates.The controlled qudit gates may be realized with the interactions of internal and external coordinates of the ion.

关键词：量子计算通用电路子电路模型搜索算法 it系统逻辑门电路离子阱自由参数

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