Machine Learning-based Intelligent Distributed Network Framework for Secure 5G-enabled IoT = 안전한 5G 중심 IoT 를 위한 머신러닝 기반 지능형 분산 네트워크 프레임워크

논문상세정보
' Machine Learning-based Intelligent Distributed Network Framework for Secure 5G-enabled IoT = 안전한 5G 중심 IoT 를 위한 머신러닝 기반 지능형 분산 네트워크 프레임워크' 의 주제별 논문영향력
논문영향력 선정 방법
논문영향력 요약
주제
  • Fifth Generation (5G)
  • Machine Learning
  • internet of things(iot)
  • security
동일주제 총논문수 논문피인용 총횟수 주제별 논문영향력의 평균
2,303 0

0.0%

' Machine Learning-based Intelligent Distributed Network Framework for Secure 5G-enabled IoT = 안전한 5G 중심 IoT 를 위한 머신러닝 기반 지능형 분산 네트워크 프레임워크' 의 참고문헌

  • [95] Health insurance portability and accountability act. Accessed May 2020 [Online]. Available: https://www.hhs.gov/hipaa/index.html
  • [91] The Future of Data with the Rise of the IoT. Accessed May 2020 [Online]. Available: https://www.rfidjournal.com/articles/view?17954
  • [8] 5G Security Market by Technology, Solution, Category, Software, Services, and Industry Vertical Support 2020–2025. Accessed: May. 2020 [Online]. Available: https://www.researchandmarkets.com/reports/4846259/5gsecurity-market-by-technology-solution
  • [87] Class jrip. Accessed May 2020 [Online]. Available: http://weka.sourceforge.net/doc.stable/weka/classifiers/rules/JRip.html
  • [83] Basic elm algorithms. Accessed May 2020 [Online]. Available: http://www3.ntu.edu.sg/home/egbhuang/elm codes.html
  • [82] Mathworks, fcm. Accessed May 2020 [Online]. Available: https://kr.mathworks.com/help/fuzzy/fcm.html
  • [81] Noxrepo, “noxrepo/pox,” GitHub,” Accessed May 2020 [Online]. Available: https://github.com/noxrepo/pox
  • [80] Team, M. “Mininet Walkthrough, Mininet: An Instant Virtual Network on your Laptop (or other PC) – Mininet,” Accessed May 2020 [Online]. Available: http://mininet.org/walkthrough/
  • [72] Hp study reveals 70 percent of internet of things devices vulnerable to attack. Accessed May 2020 [Online]. Available: http://www8.hp.com/sg/en/hp-news/press release.html
  • [6] The Growth in Connected IoT Devices Is Expected to Generate 79.4ZB of Data in 2025, According to a New IDC Forecast. Accessed: May. 2020 [Online]. Available: https://www.idc.com/getdoc.jsp?containerId=prUS45213219
  • [64] Deep Autoencoders using TensorFlow. Accessed May 2020 [Online]. Available:https://towardsdatascience.com/deep-autoencoders-usingtensorflow-c68f075fd1a3
  • [5] Ericsson Mobility Report. Accessed: May. 2020 [Online]. Available: https://www.ericsson.com/en/mobility-report/reports/november-2019
  • [51] 3GPP work taken on-line. Accessed May 2020 [Online]. Available: https://www.3gpp.org/news-events/2108-3gpp
  • [48] Number of Connected Devices Worldwide 2012-2025, Statista. Accessed May 2020 [Online]. Available:https://www.statista.com/statistics/471264/iot-number-ofconnected-devices-worldwide/
  • [3] 5G IoT Market. Accessed: May. 2020 [Online]. Available: https://www.marketsandmarkets.com/Market-Reports/5g-iot-market164027845.html
  • [28] Unlocking the value of distributed ledger technology (blockchain) in real estate. Accessed: May. 2020 [Online]. Available: https://blog.kpmg.lu/unlocking-the-value-of-distributed-ledgertechnology-blockchain-in-real-estate/
  • [26] How does a transaction get into the blockchain? Accessed: May. 2020 [Online]. Available: https://www.euromoney.com/learning/blockchainexplained/how-transactions-get-into-the-blockchain
  • [14] Introduction to Machine Learning with scikit-learn. Accessed: May. 2020 [Online]. Available:https://machinelearningmastery.com/introductionmachine-learning-scikit-learn/
  • [13] 5G Technology-A New Revolution in Industry. Accessed: May. 2020 [Online]. Available: http://saiinfosolution.blogspot.com/2019/10/
  • Wireless Device-to-Device Caching Networks : Basic Principles and System Performance
    34 ( 1 ) , pp . 176–189 [2015]
  • When wireless security meets machine learning : Motivation , challenges , and research directions
    pp . 1-8 [2020]
  • Wearable Computing for Defence Automation : Opportunities and Challenges in 5G Network
    8 , pp . 65993-66002 [2020]
  • Using the cumulative sum algorithm against distributed denial of service attacks in internet of things
    pp . 62-72 [2015]
  • Traffic-flow analysis for source-side DDoS recognition on 5G environments
    136 , pp . 114-131 [2019]
  • Towards connected living : 5G enabled internet of things ( IoT )
    36 ( 2 ) , pp . 190-202 [2019]
  • Toward smart and cooperative edge caching for 5g networks : A deep learning based approach
    pp . 1-6 [2018]
  • Svdfeature : a toolkit for feature-based collaborative filtering ,
    13 , pp . 3619-3622 [2012]
  • SoftEdgeNet : SDN based energy-efficient distributed network architecture for edgeComputing
    56 ( 12 ) , pp . 104-111 [2018]
  • Socially Motivated DataCaching in Ultra-Dense SmallCell Networks
    31 ( 4 ) , pp . 42-48 [2017]
  • Sharing decryption in theContext of voting or lotteries
    pp . 90-104 [2000]
  • Semi-supervisedClustering by seeding
    pp . 19-26 [2002]
  • Semi-supervised learning based distributed attack detection framework for IoT
    72 , pp . 79-89 [2018]
  • Ridge estimators in logistic regression
    41 ( 1 ) , pp . 191-201
  • Resource management in fog/edgeComputing : a survey on architectures , infrastructure , and algorithms ,
    52 ( 5 ) , pp . 1-37 [2019]
  • Reinforcement learning , fast and slow
    23 ( 5 ) , pp . 408-422 [2019]
  • ProactiveContentCaching by exploiting transfer learning for mobile edgeComputing
    31 ( 11 ) , pp . 1-13 [2018]
  • Privacypreserving deep learning via additively homomorphic encryption
    13 ( 5 ) , pp . 1333-1345 [2017]
  • Practical Byzantine fault tolerance
    99 , pp . 173-186 [1999]
  • Pattern Recognition with Fuzzy Objective Function Algorithms ,
    pp . 1-253 [2013]
  • Particle swarm Optimized Density-based Clustering and Classification : Supervised and unsupervised learning approaches
    44 , pp . 876-896 [2019]
  • On the capacity of additive white mixture Gaussian noise channels
    30 ( 7 ) , pp . 3585 [2019]
  • Mobile edge computing , fog et al . : A survey and analysis of security threats and challenges
    78 ( 2 ) , pp . 680-698 [2018]
  • Machine learning-based IDS for software-defined 5G network
    7 ( 2 ) , pp . 53-60 [2017]
  • Living on the Edge : The role of proactive caching in 5G wireless networks
    5 ( 8 ) , pp . 82–89 [2014]
  • Learning distance functions using equivalence relations
    pp . 11–18 [2003]
  • LIBSVM : A library for support vector machines
    2 ( 3 ) , pp . 1-27 [2011]
  • K-nearest neighbour
    4 ( 2 ) , pp . 1883-1895 [2009]
  • Introduction to machine learning
    pp . 1-665 [2020]
  • Internet of things in the 5G era : Enablers , architecture and business models
    34 ( 3 ) , pp . 510–27 [2016]
  • Internet of things : A survey on enabling technologies , protocols and applications
    17 ( 4 ) , pp . 2347–76 [2015]
  • Intelligence and security in big 5G-oriented IoNT : An overview
    102 , pp . 357-368 [2020]
  • Information Propagation in the Bitcoin Network
    pp . 1-10 [2013]
  • Hybrid clouds for dataIntensive , 5G-Enabled IoT applications : an overview , key issues and relevant architecture
    19 ( 16 ) , pp . 3591-3610 [2019]
  • From a human-centric perspective : What might 6G be ?
    pp . 1-13 [2019]
  • Federated deep reinforcement learning for internet of things with decentralized cooperative edge caching
    pp . 1-14 , DOI : 10.1109/JIOT.2020.2986803 [2020]
  • Extreme learning machine : theory and applications
    70 ( 1-3 ) , pp . 489-501 [2006]
  • Evaluation of aChannel assignment scheme in mobile network systems
    6 ( 1 ) , pp . 21–36 [2016]
  • Edge Intelligence and Blockchain Empowered 5G Beyond for the Industrial Internet of Things
    33 ( 5 ) , pp . 12-19 [2019]
  • Distributed attack detection scheme using deep learning approach for Internet of Things
    82 , pp . 761-768 [2018]
  • Detection of Unauthorized IoT Devices Using Machine Learning Techniques
    pp . 1-13 [2017]
  • Deepcachnet : A proactive caching framework based on deep learning in cellular networks
    33 ( 3 ) , pp . 130-138 [2019]
  • Deep learning-based intrusion detection with adversaries
    6 , pp . 38367-38384 [2018]
  • Deep learning in mobile and wireless networking : A survey
    21 ( 3 ) , pp . 2224-2287 [2019]
  • Deep learning for IoT big data and streaming analytics : A survey
    20 ( 4 ) , pp . 2923-2960 [2018]
  • Deep learning : The frontier for distributed attack detection in fog-to-things computing
    56 ( 2 ) , pp . 169-175 [2018]
  • Deep Reinforcement Learning-Based Edge Caching in Wireless Networks
    6 ( 1 ) , pp . 48-61 [2020]
  • Deep Learning
    521 , pp . 1-436 [2015]
  • Decision support for blockchain platform selection : Three industry case studies
    pp . 1-20 , DOI : 10.1109/TEM.2019.2956897 [2020]
  • Decentralizing privacy : Using blockchain to protect personal data
    pp . 180-184 [2015]
  • Collaborative deep learning for recommender systems
    pp . 1235–1244 [2015]
  • Classification and regression by random forest
    2 ( 3 ) , pp . 18–22 [2002]
  • Can machine learning aid in delivering new use cases and scenarios in 5G , NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium
    pp . 1279-1284 [2016]
  • CIoT-Net : a scalable cognitive IoT based smart city network architecture
    9 ( 1 ) , pp . 1-29 [2019]
  • Blockchain-based secure and trustworthy Internet of Things in SDN-enabled 5G-VANETs
    7 , pp . 56656-56666 [2019]
  • Blockchain-Based Secure Storage Management with Edge Computing for IoT
    8 ( 8 ) , pp . 828-840 [2019]
  • Blockchain for 5G-enabled IoT for industrial automation : A systematic review , solutions , and challenges
    135 , pp . 106382-106403 [2020]
  • Blockchain for 5G : opportunities and challenges
    pp . 1-6 [2019]
  • Blockchain and deep reinforcement learning empowered intelligent 5g beyond
    33 ( 3 ) , pp . 10-17 [2019]
  • Blockchain Technology for the 5G-Enabled Internet of Things Systems : Principle , Applications and Challenges , Y. Wu et al . ( Ed. ) . 5G-Enabled Internet of Things
    pp . 1- 31 [2019]
  • BlockSecIoTNet : Blockchain-based decentralized security architecture for IoT network
    143 , pp . 167-177 [2019]
  • BlockDeepNet : A BlockchainBased Secure Deep Learning for IoT Network
    11 ( 14 ) , pp . 3974-3989 [2019]
  • Big data meets telcos : A proactive caching perspective
    17 ( 6 ) , pp . 549- 557 [2015]
  • An empirical study of the naive Bayes classifier , In IJCAI 2001Workshop on Empirical Methods in Artificial Intelligence
    3 , pp . 41–46 [2001]
  • An efficient deep learning model for intrusion classification and prediction in 5G and IoT networks
    pp . 1-6 [2019]
  • Advancing the State of the Fog Computing to Enable 5G Network Technologies
    20 ( 6 ) , pp . 1754-1780 [2020]
  • A two-layer dimension reduction and two-tier classification model for anomaly-based intrusion detection in IoT backbone networks
    7 ( 2 ) , pp . 314-323 [2016]
  • A transfer learning approach for cache-enabled wireless networksIn Proceedings of the 2015 13th International Symposium on Modeling and Optimization in Mobile , Ad Hoc , and Wireless Networks ( WiOpt )
    pp . 161-166 [2015]
  • A study on semisupervised FCM algorithm
    35 ( 3 ) , pp . 585–612 [2013]
  • A possibilistic fuzzy c-means clustering algorithm
    13 ( 4 ) , pp . 517–530 [2005]
  • A novel framework for internet of knowledge protection in social networking services
    26 , pp . 55-65 [2018]
  • A multi-layer security model for 5G-enabled industrial Internet of Things
    pp . 279-292 [2019]
  • A detailed analysis of the kddcup 99 data set
    pp . 1–6 [2009]
  • A deep learning approach for optimizing content delivering in cacheenabled HetNet
    pp . 449-453 [2017]
  • A comparative study of collaborative filtering algorithms ,
    pp . 1-27 [2012]
  • A Survey on Security and Privacy of 5G Technologies : Potential Solutions , Recent Advancements , and Future Directions
    22 ( 1 ) , pp . 196-248 [2019]
  • 5G-enabled Internet of Things
    pp . 1-389 [2019]
  • 5G support for Industrial IoT Applications–Challenges , Solutions , and Research gaps
    20 ( 3 ) , pp . 8-28 [2020]
  • 5G in the Internet of Things era : an overview on security and privacy challenges.
    179 , pp . 107345-107357 [2020]
  • 5G Internet of Things : A survey
    10 , pp . 1-9 [2018]