Blockchain based cloud service security architecture with distributed machine learning for smart device traffic record transaction

被引:3
作者
Pon, Partheeban [1 ]
Kavitha, V [2 ]
机构
[1] Stella Marys Coll Engn, Dept Comp Sci & Engn, Kanyakumari, Tamil Nadu, India
[2] Univ Coll Engn, Dept Comp Sci & Engn, Kancheepuram, India
关键词
blockchain; cloud service; machine learning; security attacks; smart devices; transaction network;
D O I
10.1002/cpe.6583
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
In today's world, the transaction through the smart device has received greater attention and configures numerous applications which can efficiently process huge traffic records on a growing demand for service centers from the edge of the networks. Due to these immense growths, the concern raises in critical transaction records in terms of system security threats and efficiency issues in the smart devices. However, existing methods failed due to security attacks during the tenure of access transactions and aggregated services. Recently, blockchain technology enables service centers depends on various platforms to share transaction records. But, it is difficult to store the transaction record because of its size. To address these issues, we proposed a SECure LearningChain (SEC-LearningChain) design based on the integration of blockchain technology, machine learning (ML), and cloud computing primitives are applied together for a secure data transaction in a Peer to Peer network as well as efficient data sharing service. This approach consists of four design models: First, an attack detection model detects the attack using threshold-based anomalous traffic detector in the transaction network. Second, a mold blockchain transaction network model is designed based on the cryptographic hash and encryption to deal with threats and validate the identity verification process for a secure transaction. Next, the large-scale transaction record is optimized and trains the ML model for the output prediction. Finally, the cloud assessment model manages the stored transaction records and easily share the accessed services across different cloud platforms for each service center. Furthermore, we prove that the SEC-LearningChain design resists transmission control protocol flooding attack, denial of service attack, and falsify attack. Experimental results demonstrate that the performance of the SEC-LearningChain achieves more number of transactions in each blocks over existing schemes.
引用
收藏
页数:21
相关论文
共 34 条
  • [1] NMCDA: A framework for evaluating cloud computing services
    Abdel-Basset, Mohamed
    Mohamed, Mai
    Chang, Victor
    [J]. FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2018, 86 : 12 - 29
  • [2] Cloud based security on outsourcing using blockchain in E-health systems
    Benil, T.
    Jasper, J.
    [J]. COMPUTER NETWORKS, 2020, 178
  • [3] CASAS: A Smart Home in a Box
    Cook, Diane J.
    Crandall, Aaron S.
    Thomas, Brian L.
    Krishnan, Narayanan C.
    [J]. COMPUTER, 2013, 46 (07) : 62 - 69
  • [4] Desai SA, 2019, I IEEE EMBS C NEUR E, P1, DOI [10.1109/NER.2019.8717007, 10.1109/ner.2019.8717007]
  • [5] Efficient Privacy-Preserving Machine Learning for Blockchain Network
    Kim, Hyunil
    Kim, Seung-Hyun
    Hwang, Jung Yeon
    Seo, Changho
    [J]. IEEE ACCESS, 2019, 7 : 136481 - 136495
  • [6] Detecting multivariate outliers: Use a robust variant of the Mahalanobis distance
    Leys, Christophe
    Klein, Olivier
    Dominicy, Yves
    Ley, Christophe
    [J]. JOURNAL OF EXPERIMENTAL SOCIAL PSYCHOLOGY, 2018, 74 : 150 - 156
  • [7] Blockchain-based Security Architecture for Distributed Cloud Storage
    Li, Jiaxing
    Liu, Zhusong
    Chen, Long
    Chen, Pinghua
    Wu, Jigang
    [J]. 2017 15TH IEEE INTERNATIONAL SYMPOSIUM ON PARALLEL AND DISTRIBUTED PROCESSING WITH APPLICATIONS AND 2017 16TH IEEE INTERNATIONAL CONFERENCE ON UBIQUITOUS COMPUTING AND COMMUNICATIONS (ISPA/IUCC 2017), 2017, : 408 - 411
  • [8] Secure attribute-based data sharing for resource-limited users in cloud computing
    Li, Jin
    Zhang, Yinghui
    Chen, Xiaofeng
    Xiang, Yang
    [J]. COMPUTERS & SECURITY, 2018, 72 : 1 - 12
  • [9] Privacy-preserving machine learning with multiple data providers
    Li, Ping
    Li, Tong
    Ye, Heng
    Li, Jin
    Chen, Xiaofeng
    Xiang, Yang
    [J]. FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2018, 87 : 341 - 350
  • [10] Privacy-preserving outsourced classification in cloud computing
    Li, Ping
    Li, Jin
    Huang, Zhengan
    Gao, Chong-Zhi
    Chen, Wen-Bin
    Chen, Kai
    [J]. CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 2018, 21 (01): : 277 - 286