计算机科学 ›› 2019, Vol. 46 ›› Issue (6): 1-20.doi: 10.11896/j.issn.1002-137X.2019.06.001

• 大数据与数据科学* •    下一篇

大数据分析技术在网络领域中的研究综述

冯贵兰1, 李正楠2, 周文刚3   

  1. (中国民航飞行学院现代教育技术中心 四川 广汉 618307)1
    (中国民航飞行学院航空工程学院 四川 广汉 618307)2
    (中国民航飞行学院飞行技术学院 四川 广汉 618307)3
  • 收稿日期:2018-11-18 发布日期:2019-06-24
  • 通讯作者: 冯贵兰(1988-),女,硕士生,工程师,主要研究领域为大数据与网络安全,E-mail:fengguilan1016@sina.com
  • 作者简介:李正楠(1985-),男,主要研究领域为网络与信息安全;周文刚(1981-),男,博士生,讲师,主要研究领域为网络管理、人工智能等。
  • 基金资助:
    民航飞行数据分析研究项目(XM2852)资助。

Research on Application of Big Data Analytics in Network

FENG Gui-lan1, LI Zheng-nan2, ZHOU Wen-gang3   

  1. (Modern Education Technology Center,Civil Aviation Flight University of China,Guanghan,Sichuan 618307,China)1
    (Institute of Aviation Engineering,Civil Aviation Flight University of China,Guanghan,Sichuan 618307,China)2
    (Institute of Flight Technology,Civil Aviation Flight University of China,Guanghan,Sichuan 618307,China)3
  • Received:2018-11-18 Published:2019-06-24

摘要: 随着移动互联网、物联网、5G通信网等新兴技术的迅猛发展,数以亿计的网络接入点、联网设备以及网络应用产生的海量数据,给网络故障排查、网络安全保障等带来了极大的挑战,同时也为人们深度挖掘和充分利用网络大数据的巨大价值带来了机遇。大数据分析可以处理海量数据,并从中抽取有价值的潜在知识,帮助决策者发现隐藏的关系和模式,近年来引起了学术界和工业界的广泛关注。文中围绕大数据分析技术应用于网络领域的最新研究成果,首先阐述了网络大数据的概念、分类和数据分析方法;然后从无线网络、SDN网络、光纤网络和网络安全4个层面着重介绍了大数据分析技术在故障检测、流量监控、网络优化、流量预测、APT攻击检测、网络异常检测等网络领域中的解决方案,重点分析和归纳了这些解决方案中大数据分析技术的思路;接着回顾了大数据分析技术在工业界中应用的情况;在此基础上,给出了基于大数据分析的网络设计周期;最后总结了大数据分析技术在网络领域中面临的机遇和挑战,并指出下一步需要关注的研究方向。

关键词: 大数据分析, 网络优化, 频谱管控, 流量预测, 网络安全

Abstract: With the rapid development of new technologies like mobile internet,Internet of Things and 5G communication network,more and more infrastructures,devices and data are generated,such as hundreds of millions of network access points,networked devices,applications as well as massive data.Thus,great difficulties and challenges are brought to fault tolerance,cyberspace security,leading to some traditional solutions become inefficient to such large scale and complex security problems.Meanwhile,the increase of network big data presents unprecedented opportunities on deeply mining and taking full advantage of the big value of network big data.Big data analytics can extract hidden,valuable patterns,and useful information from big data.Therefore,both academia and industry have been attracted again by network field based on big data analytics,and have made certain research achievement.Researches on network field mainly involve four research directions,namely wireless network,SDN network,optical network and cyberspace security.First,the survey starts with the introduction of the big data basic concepts,data model and data analytics.Second,there is a detailed review of the current academic and industrial efforts toward network design using big data analytics.Third,the main network design cycle is illustrated by employing big data analytics.This cycle represents the umbrella concept that unifies the surveyed topics.Forth,the challenges confronted by the utilization of big data analytics in network design are identified.Finally,several future research directions are highlighted.

Key words: Big data analytics, Network optimization, Spectrum management, Traffic prediction, Cyberspace security

[1]HADI M,LAWEY A Q,EL-GORASHI T,et al.Big Data Analytics for Wireless and Wired Network Design:A Survey[J].Computer Networks,2018,132:180-199.
[2]HE Z,CAI Z,YU J.Latent-data Privacy Preserving with Cus-tomized Data Utility for Social Network Data[J].IEEE Tran-sactions on Vehicular Technology,2017,67(1):665-673.
[3]BI S,ZHANG R,DING Z,et al.Wireless communications in the era of big data[J].IEEE Communications Magazine,2015,53(10):190-199.
[4]QIAN L,ZHU J,ZHANG S.Survey of wireless big data[J].Journal of Communications & Information Networks,2017,2(1):1-18.
[5]XU Q S,GE L Q,ZOU Q Y.Wireless Communication Techno-logy based on Big-Data Analysis[J].Communications Technology,2016,49(12):1635-1641.(in Chinese)
徐全盛,葛林强,邹勤宜.基于大数据分析的无线通信技术研究[J].通信技术,2016,49(12):1635-1641.
[6]FU Y,LI H C,WU X P,et al.Detecting APT attacks:a survey from the perspective of big data analysis[J].Journal on Communications,2015,36(11):1-14.(in Chinese)
付钰,李洪成,吴晓平,等.基于大数据分析的APT攻击检测研究综述[J].通信学报,2015,36(11):1-14.
[7]CHEN X S,ZENG X M,WANG W X,et al.Big Data Analytics for Network Security and Intelligence[J].Advanced Engineering Sciences,2017,49(3):1-12.(in Chinese)
陈兴蜀,曾雪梅,王文贤,等.基于大数据的网络安全与情报分析[J].工程科学与技术,2017,49(3):1-12.
[8]WANG Y Z,JIN X L,CHENG X Q.Network Big Data:Present and Future[J].Chinese Journal of Computers,2013,36(6):1125-1138.(in Chinese)
王元卓,靳小龙,程学旗.网络大数据:现状与展望[J].计算机学报,2013,36(6):1125-1138.
[9]CHEN K F,ZHOU H C,AMP J P.Research on Realization Mode of Telecom Operators’ Big Data Resource and its Strategy[J].Mobile Communications,2016,40(1):63-67.
[10]ZHANG X,YI Z,YAN Z,et al.Social Computing for Mobile Big Data[J].Computer,2016,49(9):86-90.
[11]CHEN M,MAO S,LIU Y.Big Data:A Survey[J].Mobile Networks & Applications,2014,19(2):171-209.
[12]HE Y,YU F R,ZHAO N,et al.Big Data Analytics in Mobile Cellular Networks[J].IEEE Access,2016,4:1985-1996.
[13]ZHANG C,QIU R C.Massive MIMO as a Big Data System:Random Matrix Models and Testbed[J].IEEE Access,2015,3:837-851.
[14]KUANG L,HAO F,YANG L T,et al.A Tensor-Based Ap-proach for Big Data Representation and Dimensionality Reduction[J].IEEE Transactions on Emerging Topics in Computing,2017,2(3):280-291.
[15]XU F,LIN Y,HUANG J,et al.Big data driven mobile traffic understanding and forecasting:a time series approach[J].IEEE Transactions on Services Computing,2016,9(5):796-805.
[16]MURPHY K.Machine learning:a probabilistic perspective[M].Cambridge:MIT Press,2012.
[17]GOODFELLOW I,BENGIO Y,COURVILLE A.Deep Learning[M].Cambridge:MIT Press,2016.
[18]DONAHUE J,HENDRICKS L,GUADARRAMA S,et al. Longterm recurrent convolutional networks for visual recognition and description [C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2015:2625-2634.
[19]ZHANG Q,YANG L T,CHEN Z,et al.A survey on deep lear-ning for big data[J].Information Fusion,2018,42:146-157.
[20]BUCZAK A L,GUVEN E.A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection[J].IEEE Communications Surveys & Tutorials,2017,18(2):1153-1176.
[21]ZHANG L,CUI Y,LIU J,et al.Application of Machine Lear-ning in Cyberspace Security Research[J].Chinese Journal of Computers,2018,41:1-35.(in Chinese)
张蕾,崔勇,刘静,等.机器学习在网络空间安全研究中的应用[J].计算机学报,2018,41:1-35.
[22]ALSHEIKH M A,NIYATO D,LIN S,et al.Mobile big data ana-lytics using deep learning and apache spark [J].IEEE Network,2016,30(3):22-29.
[23]MA Q,ZHANG S,ZHOU W,et al.When Will You Have a New Mobile Phone? An Empirical Answer From Big Data[J].IEEE Access,2016,4:10147-10157.
[24]Yang C.Learning methodologies for wireless big data networks:a Markovian game-theoretic perspective [J].Neurocomputing,2016,174:431-438.
[25]LANDSET S,KHOSHGOFTAAR T M,RICHTER A N,et al.A survey of open source tools for machine learning with big data in the Hadoop ecosystem[J].Journal of Big Data,2015,2 (1):24.
[26]Apache SparkTM-lightning-fast cluster computing[EB/OL].
[2017-03-20].http://spark.apache.org/.
[27]CELEBI O F,ZEYDAN E,KURT O F,et al.On use of big data for enhancing network coverage analysis[C]∥ International Conference on Telecommunications.IEEE,2013:646-655.
[28]GAO J,CHENG X,XU L,et al.A downlink coverage self-optimizing algorithm for LTE cellular networks based on big data analytics[C]∥Proceedings of the 3rd International Conference on Signal and Information Processing,Networking and Compu-ters.Springer,2017:373-380.
[29]KARATEPE I A,ZEYDAN E.Anomaly Detection In Cellular Network Data Using Big Data Analytics[C]∥ Proceedings of VDE.2014:1-5.
[30]PARWEZ M S,RAWAT D B,GARUBA M.Big Data Analytics for User Activity Analysis and User Anomaly Detection in Mobile Wireless Network[J].IEEE Transactions on Industrial Informatics,2017,PP(99):1-1.
[31]YANG K,LIU R,SUN Y,et al.Deep Network Analyzer (DNA):A Big Data Analytics Platform for Cellular Networks[J].IEEE Internet of Things Journal,2017,4(6):2019-2027.
[32]SAHNI A,MARWAH D,CHADHA R.Real time monitoring and analysis of available bandwidth in cellular network-using big data analytics[C]∥ International Conference on Computing for Sustainable Global Development.IEEE,2015:1743-1747.
[33]KHATIB E J,BARCO R,MUNOZ P,et al.Self-healing in mobile networks with big data[J].Communications Magazine IEEE,2016,54(1):114-120.
[34]IMRAN A,ZOHA A.Challenges in 5G:how to empower SON with big data for enabling 5G[J].Network IEEE,2014,28(6):27-33.
[35]JIANG D,HUO L,SONG H.Rethinking behaviors and activities of base stations in mobile cellular networks based on big data analysis[J/OL].IEEE Transactions on Network Science and Engineering,https://ieeexplore.ieee.org/abstract/document/8423194.
[36]LIU J,LIU F,ANSARI N.Monitoring and analyzing big traffic data of a large-scale cellular network with Hadoop[J].Network IEEE,2014,28(4):32-39.
[37]PALACIO A F,WAUTERS T,VOLCKAERT B,et al.Scalable distributed traffic monitoring for enterprise networks with Spark Streaming[C]∥European Conference on Cyber Warfare and Security.2018:563-570.
[38]QIAO Y,XING Z,FADLULLAH Z M,et al.Characterizing Flow,Application,and User Behavior in Mobile Networks:A Framework for Mobile Big Data[J].IEEE Wireless Communications,2018,25(1):40-49.
[39]BAS,TUSˇ E,BENNIS M,ZEYDAN E,et al.Big data meets telcos:A proactive caching perspective[J].Journal of Communications and Networks,2016,17(6):549-557.
[40]ZEYDAN E,BASTUG E,BENNIS M,et al.Big data caching for networking:moving from cloud to edge[J].IEEE Communications Magazine,2016,54(9):36-42.
[41]OMAR A.Improving Data Extraction Efficiency of Cache Nodes in Cognitive Radio Networks Using Big Data Analysis[C]∥ International Conference on Next Generation Mobile Applications,Services and Technologies.IEEE,2016:305-310.
[42]FAN B,LENG S,YANG K.A dynamic bandwidth allocation algorithm in mobile networks with big data of users and networks[J].IEEE Network,2016,30(1):6-10.
[43]WANG L,WANG X D,CHENG N.Intelligent System of Wireless Network Optimization Based on Big Data Technology[J].Telecommunications Science,2015,31(12):159-163.(in Chinese)
王磊,王西点,程楠.基于大数据技术的智能化无线网络优化体系[J].电信科学,2015,31(12):159-163.
[44]LEE C L,SU W S,TANG K A,et al.Design of handover self-optimization using big data analytics[C]∥ Network Operations and Management Symposium.IEEE,2014:1-5.
[45]CHIH-LIN I,LIU Y,HAN S,et al.On Big Data Analytics for Greener and Softer RAN[J].IEEE Access,2016,3(94):3068-3075.
[46]LIU Y,LIU K,KONG J K.TD-LTE Network Planning based on Big Data Mining[J].Communications Technology,2015,48(2):194-198.(in Chinese)
刘毅,刘珂,孔建坤.基于大数据挖掘的LTE网络规划研究[J].通信技术,2015,48(2):194-198.
[47]ZHENG K,YANG Z,ZHANG K,et al.Big data-driven optimization for mobile networks toward 5G[J].IEEE Network,2016,30(1):44-51.
[48]XU C,YANG J,YU H,et al.Optimizing the Topologies of Virtual Networks for Cloud-Based Big Data Processing[C]∥ High PERFORMANCE Computing and Communications,2014 IEEE,Intl Symp on Cyberspace Safety and Security,2014 IEEE,Intl Conf on Embedded Software and Syst.IEEE,2014:189-196.
[49]KIRAN P,JIBUKUMAR M G,PREMKUMAR C V.Resource allocation optimization in LTE-A/5G networks using big data analytics[C]∥ International Conference on Information Networking.IEEE Computer Society,2016:254-259.
[50]XU R P,CUI J Y,GUAN Z L,et al.Design and application of Wireless Network optimal data sharing architecute[J].Telecommunications Science,2016,32(4):152-158.(in Chinese)
许汝鹏,崔晶也,关则洛,等.网优大数据共享架构设计及应用实践[J].电信科学,2016,32(4):152-158.
[51]LI Y.Grass-root based SpectrumMap database for self-organized cognitive radio and heterogeneous networks:Spectrum measurement,data visualization,and user participating model[C]∥ Wireless Communications and Networking Conference (WCNC),2015 IEEE.IEEE,2015:117-122.
[52]WU Q,DING G,DU Z,et al.A Cloud-Based Architecture for the Internet of Spectrum Devices Over Future Wireless Networks[J].IEEE Access,2017,4:2854-2862.
[53]GVK S,DASARI S R.Big Spectrum Data Analysis in DSA Enabled LTE-A Networks:A System Architecture[C]∥ IEEE,International Conference on Advanced Computing.IEEE,2016:655-660.
[54]ZHU Q,ZHANG X.Effective-capacity based gaming for optimal power and spectrum allocations over big-data virtual wireless networks [C]∥The IEEE Global Communications Conference (GLOBECOM).IEEE,2015:1-6.
[55]LI D.Application of Spectrum Data Management based on Bayesian Network[J].International Journal of Future Generation Communication and Networking,2015,8(7):13-24.
[56]BALTIISKI P,ILIEV I,KEHAIOV B,et al.Long-Term Spectrum Monitoring with Big Data Analysis and Machine Learning for Cloud-Based Radio Access Networks[J].Wireless Personal Communications,2016,87(3):815-835.
[57]VASSAKIS K,PETRAKIS E,KOPANAKIS I.Big Data Ana-lytics:Applications,Prospects and Challenges[M]//Mobile Big Data.Cham:Springer,2018.
[58]CHIU P,REUNANEN J,LUOSTARI R,et al.Big Data Analytics for 4.9G and 5G Mobile Network Optimization[C]∥ Vehicular Technology Conference.IEEE,2017:1-4.
[59]YAN Q,CHEN W,POOR H V.Big Data Driven Wireless Communications:A Human-in-the-Loop Pushing Technique for 5G Systems[J].IEEE Wireless Communications,2018,25(1):64-69.
[60]RAZA M R,NATALINO C,VIDAL A.Demonstration of Resource Orchestration Using Big Data Analytics for Dynamic Slicing in 5G Networks[C]∥2018 European Conference on Optical Communication.2018.
[61]ZHANG N,YANG P,REN J,et al.Synergy of Big Data and 5G Wireless Networks:Opportunities,Approaches,and Challenges[J].IEEE Wireless Communications,2018,25(1):12-18.
[62]RAMASWAMI R,SIVARAJAN K N.Routing and wavelength assignment in all-optical networks[J].IEEE/ACM Transactions on Networking (TON),1995,3(5):489-500.
[63]YAN S F.Heuristic Algorithm for Routing and Wavelength Assignment Problem [D].Wuhan:Huazhong University of Science and Technology,2016.(in Chinese)
燕圣峰.基于启发式算法求解路由与波长分配问题[D].武汉:华中科技大学,2016.
[64]SHEN G,LI Y,PENG L,et al.Almost-optimal design for optical networks with hadoop cloud computing:Ten ordinary desktops solve 500-node,1000-link,and 4000-request RWA problem within three hours∥2013 15th International Conference on Transparent Optical Networks (ICTON).IEEE,2013:1-4.
[65]LI Y,SHEN G,CHEN B,et al.Applying Hadoop Cloud Computing Technique to Optimal Design of Optical Networks[C]∥Asia Communications & Photonics Conference.Optical Society of America,2015:Asu3H.3.
[66]AUTENRIETH A,AGUADO A,MAYORAL A,et al.Dynamic Virtual Network Reconfiguration Over SDN Orchestrated Multitechnology Optical Transport Domains[J].Journal of Lightwave Technology,2016,34(8):1933-1938.
[67]MORALES F,RUIZ M,GIFRE L,et al.Virtual network topo-logy adaptability based on data analytics for traffic prediction[J].IEEE/OSA Journal of Optical Communications & Networking,2017,9(1):A35-A45.
[68]QADIR J,AHAD N,MUSHTAQ E,et al.SDNs,Clouds,and Big Data:New Opportunities[C]∥ International Conference on Frontiers of Information Technology.IEEE Computer Society,2014:28-33.
[69]MCKEOWN N,ANDERSON T,BALAKRISHNAN H,et al.OpenFlow:enabling innovation in campus networks[J].ACM SIGCOMM Computer Communication Review,2008,38(2):69-74.
[70]CUI H,ZHANG Y,MA C,et al.Design and Realization of Cognitive Routing Resources Using Big Data Analysis in SDN[C]∥ IEEE International Congress on Big Data.IEEE Computer Socie-ty,2015:424-429.
[71]NEVES M V,KATRINIS K,FRANKE H.Pythia:Faster Big Data in Motion through Predictive Software-Defined Network Optimization at Runtime[C]∥ Parallel and Distributed Proces-sing Symposium,2014 IEEE,International.IEEE,2014:82-90.
[72]COSTA P,DONNELLY A,ROWSTRON A,et al.Camdoop: Exploiting In-network Aggregation for Big Data Applications[C]∥NSDI.2012.
[73]COSTA P,DONNELLY A,O’SHEA G,et al.CamCube:a key-based data center:Technical Report MSR TR-2010-74[R].Microsoft Res.,Redmond,WA,USA,2010.
[74]ISARD M,BUDIU M,YU Y,et al.Dryad:distributed data-pa-rallel programs from sequential building blocks[C]∥ACM SIGOPS Operating Systems Review.ACM,2007:59-72.
[75]YU Y,ISARD M,FETTERLY D,et al.DryadLINQ:a system for general-purpose distributed data-parallel computing using a high-level language[C]∥ Usenix Symposium on Operating Systems Design and Implementation(OSDI 2008).San Diego,California,USA,DBLP,2008:1-14.
[76]CUI L,YU F R,YAN Q.When big data meets software-defined networking:SDN for big data and big data for SDN[J].IEEE Network,2016,30(1):58-65.
[77]GIURA P,WANG W.Using large scale distributed computing to unveil advanced persistent threats[J].Science,2013,1(3):93-105.
[78]YEN T F,OPREA A,ONARLIOGLU K,et al.Beehive:large-scale log analysis for detecting suspicious activity in enterprise networks[C]∥ Computer Security Applications Conference.ACM,2013:199-208.
[79]MARCHETTI M,PIERAZZI F,COLAJANNI M,et al.Analysis of high volumes of network traffic for Advanced Persistent Threat detection[M].Elsevier North-Holland,Inc.,2016.
[80]ZHANG X S,NIU W N,YANG G W,et al.Method for APT Prediction Based on Tree Structure[J].Journal of University of Electronic Science and Technology of China,2016,45(4):582-588.(in Chinese)
张小松,牛伟纳,杨国武,等.基于树型结构的APT攻击预测方法[J].电子科技大学学报,2016,45(4):582-588.
[81]HSIEH C J,CHAN T Y.Detection DDoS attacks based on neural-network using Apache Spark[C]∥ International Conference on Applied System Innovation.IEEE,2016:1-4.
[82]JIA B,MA Y,HUANG X H,et al.A Novel Real-Time DDoS Attack Detection Mechanism Based on MDRA Algorithm in Big Data[J].Mathematical Problems in Engineering,2016,2016:1-10.
[83]Hameed S,Ali U.HADEC:Hadoop-based live DDoS detection framework[J].Eurasip Journal on Information Security,2018,2018(1):11.
[84]HOON K S,YEO K C,AZAM S,et al.Critical review of ma-chine learning approaches to apply big data analytics in DDoS forensics[C]∥ 2018 International Conference on Computer Communication and Informatics.IEEE,2018.
[85]MYLAVARAPU G,THOMAS J,ASHWIN K T K.Real-Time Hybrid Intrusion Detection System Using Apache Storm[C]∥ IEEE International Conference on High PERFORMANCE Computing and Communications.IEEE,2015:1436-1441.
[86]RATHORE M M,AHMAD A,PAUL A.Real time intrusion detection system for ultra-high-speed big data environments[J].Journal of Supercomputing,2016,72(9):1-22.
[87]WANG L,JONES R.Big data analytics for network intrusion detection:A survey[J].International Journal of Networks and Communications,2017,7(1):24-31.
[88]STONE-GROSS B,COVA M,CAVALLARO L.Your botnet is my botnet:analysis of a botnet takeover[C]∥ACM Conference on Computer and Communications Security(CCS 2009).Chicago,Illinois,Usa,DBLP,2009:635-647.
[89]USCERT.WordPress Sites Targeted by Mass Brute-force Botnet Attack[EB/OL].https://www.us-cert.gov/.
[90]SINGH K,GUNTUKU S C,THAKUR A,et al.Big Data Analytics framework for Peer-to-Peer Botnet detection using Random Forests[J].Information Sciences,2014,278(19):488-497.
[91]CHRIS S.Applied Network Security Monitoring[M]∥Applied Network Security Monitoring:Collection,Detection,and Analysis.Syngress Publishing,2013.
[92]LIU Y,GUO S,HU S,et al.Performance Evaluation and Optimization of Multi-dimensional Indexes in Hive[J].IEEE Tran-sactions on Services Computing,2016,PP(99):1-1.
[93]TERZI D S,TERZI R,SAGIROGLU S.Big data analytics for network anomaly detection from netflow data[C]∥ Internatio-nal Conference on Computer Science and Engineering.IEEE,2017:592-597.
[94]YAO H,LIU Y,FANG C.An Abnormal Network Traffic Detection Algorithm Based on Big Data Analysis[J].International Journal of Computers,Communications & Control,2016,11(4):567-579.
[95]BACHUPALLY Y R,YUAN X,ROY K.Network security analysis using Big Data technology[C]∥ Southeastcon.IEEE,2016:1-4.
[96]PURI C,DUKATZ C.Analyzing and Predicting Security Event Anomalies:Lessons Learned from a Large Enterprise Big Data Streaming Analytics Deployment[C]∥ International Workshop on Database and Expert Systems Applications.IEEE Computer Society,2015:152-158.
[97]GUAN L,HU G J,WANG Z.Research on Network Security Situational Awareness Technology Based on Big Data[J].Netinfo Security,2016(9):45-50.(in Chinese)
管磊,胡光俊,王专.基于大数据的网络安全态势感知技术研究[J].信息网络安全,2016(9):45-50.
[98]ZHAO M.Network Security Situation Awareness Based on Big Bata[J].Netinfo Security,2016(9):90-93.(in Chinese)
赵梦.基于大数据环境的网络安全态势感知信息[J].网络安全,2016(9):90-93.
[99]XU Q,ZHENG R,SAAD W,et al.Device Fingerprinting in Wireless Networks:Challenges and Opportunities[J].IEEE Communications Surveys & Tutorials,2016,18(1):94-104.
[100]MOLINA M,PAREDES-OLIVA I,ROUTLY W,et al.Operational experiences with anomaly detection in backbone networks[J].Computers & Security,2012,31(3):273-285.
[101]RICCIATO F.Traffic monitoring and analysis for the optimization of a 3G network[J].IEEE Wireless Communications,2006,13(6):42-49.
[102]PARWEZ M S,RAWAT D B,GARUBA M.Big Data Analytics for User-Activity Analysis and User-Anomaly Detection in Mobile Wireless Network[J].IEEE Transactions on Industrial Informatics,2017,13(4):2058-2065.
[103]SPIESS J,T'JOENS Y,DRAGNEA R,et al.Using Big Data to Improve Customer Experience and Business Performance[J].Bell Labs Technical Journal,2014,18(4):3-17.
[104]JIANG Z,DAIWU S,ZHENHUA Y U.Study on NetworkFai-lure Prediction Based on Alarm Log[C]∥ Mec International Conference on Big Data & Smart City.IEEE,2016:1-7.
[105]SHUAN L H,FEI T Y,KING S W,et al.Network Equipment Failure Prediction with Big Data Analytics[J].International Journal of Advances in Soft Computing & Its Applications,2016,8(3):59-69.
[106]YANG K,LIU R,SUN Y,et al.Deep Network Analyzer (DNA):A Big Data Analytics Platform for Cellular Networks[J].IEEE Internet of Things Journal,2017,PP(99):1-1.
[107]QIAO Y,LEI Z,YANG J,et al.FLAS:Traffic analysis of emerging applications on Mobile Internet using cloud computing tools[C]∥2013 16th International Symposium on Wireless Personal Multimedia Communications (WPMC).IEEE,2013:1-6.
[108] QI G,TSAI W T,LI W,et al.A cloud-based triage log analysis and recovery framework[J].Simulation Modelling Practice and Theory,2017,77:292-316.
[109]PARK B H,HUKERIKAR S,ADAMSON R,et al.Big Data Meets HPC Log Analytics:Scalable Approach to Understanding Systems at Extreme Scale[C]∥2017 IEEE International Conference on Cluster Computing (CLUSTER).IEEE,2017:758-765.
[110]CHANG V.A proposed social network analysis platform for big data analytics[J].Technological Forecasting Social Change,2018,130:57-68.
[111]LEUNG C K,JIANG F,POON T W,et al.Big Data Analytics of Social Network Data:Who Cares Most About You on Facebook?[J].Highlighting the Importance of Big Data Management and Analysis for Various Applications,2018,27:1-15.
[112]SONG P,LU D Y,ZHAO Y P,et al.New Progress of Big Data Research in Social Network[J].Lantai World,2017(12):63-67.(in Chinese)
宋朋,陆丹玥,赵燕萍,等.社交网络中大数据研究新进展[J].兰台世界,2017(12):63-67.
[113]RATHORE M M,PAUL A,HONG W H.Exploiting IoT and big data analytics:Defining Smart Digital City using real-time urban data[J],Sustainable Cities and Society,2018,40:600-610 [114]BORJA M G,MARÍA HENAR S O,JUAN CARLOS G P, et al.Dynamic accessibility using Big Data:The role of the changing conditions of network congestion and destination attractiveness[J].arXiv:1610.06450,2016.
[115]SENARATNE H,MUELLER M,BEHRISCH M,et al.Urban Mobility Analysis With Mobile Network Data:A Visual Analy-tics Approach[J].IEEE Transactions on Intelligent Transportation Systems,2017,19(5):1-10.
[116]GOHAR M,MUZAMMAL M,RAHMAN A U.SMART TSS:Defining Transportation System Behavior using Big Data Analytics in Smart Cities[J].Sustainable Cities & Society,2018,41:114-119.
[117]TAO H.Big Data Analytics:Making the Smart Grid Smarter [J].IEEE Power & Energy Magazine,2018,16(3):12-16.
[118]WANG G,GUNASEKARAN A,NGAI E W T.Distribution network design with big data:Model and analysis[J].Annals of Operations Research,2018,270(12):539-551.
[119]MANOGARAN G,VARATHARAJAN R,LOPEZ D.A new architecture of Internet of Things and big data ecosystem for secured smart healthcare monitoring and alerting system[J].Future Generation Computer Systems,2018,82:375-387.
[120]HADI M S,LAWEY A Q,EL-GORASHI T E H,et al.Patient-Centric Cellular Networks Optimization using Big Data Analy-tics[J].IEEE Access,2019,7:49279-49296.
[121]HOSSAIN M S,MUHAMMAD G.Emotion-Aware Connected Healthcare Big Data Towards 5G[J].IEEE Internet of Things Journal,2017,PP(99):1-1.
[122]WAMBA S F,ANGAPPA G.Big data analytics in logistics and supply chain management[J].Journal of Logistics Management,2018,29(2):478-484.
[123]FIROUZI F,RAHMANI A M,MANKODIYA K,et al.Inter-net-of-Things and big data for smarter healthcare:From device to architecture,applications and analytics[J].Future Generation Computer Systems,2017,78(2018):583-586.
[124]WAHID A,SHAH M A,QURESHI F F.Big data analytics for mitigating broadcast storm in Vehicular Content Centric networks[J].Future Generation Computer Systems,2018,86:1301-1320.
[125]IMURA T,HORI Y.Maximizing Air Gap and Efficiency of Magnetic Resonant Coupling for Wireless Power Transfer Using Equivalent Circuit and Neumann Formula[J].IEEE Tran-sactions on Industrial Electronics,2011,58(10):4746-4752.
[126]FINLEY K.Internet by Satellite Is a Space Race With No Winners.https://www.wired.com/2015/06/elon-musk-space-x-satellite-internet/.
[127]LI P,CHEN Z,YANG L T,et al.Deep Convolutional Computation Model for Feature Learning on Big Data in Internet of Things[J].IEEE Transactions on Industrial Informatics,2017,PP(99):1-1.
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