Journal Home > Volume 28 , Issue 5
Tsinghua Science and Technology 2023, 28(5): 838-848 https://doi.org/10.26599/TST.2022.9010058
Open Access | Issue | Published: 19 May 2023

A Solution Supporting Secure Transmission of Big Data

Show Author's Information Haonan Ling1( ),  Yan Gao2,  Huibin Wang3,  Ming Chen1( )
Institute of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
China Electronic Systems Engineering Corporation, Beijing 100840, China
College of Computer and Information Engineering, Chuzhou University, Chuzhou 239000, China
Keywords:
network function virtualization, segment routing, network secure path, search-transmit model, multi-attribute path optimization
Cite this article:
Ling H, Gao Y, Wang H, et al. A Solution Supporting Secure Transmission of Big Data. Tsinghua Science and Technology, 2023, 28(5): 838-848. https://doi.org/10.26599/TST.2022.9010058
Download citation
EndNote(RIS)
BibTeX

450

Views

123

Downloads

Citations

1

Crossref

0

WoS

1

Scopus

0

CSCD

Abstract Full text About this article

Traditional Internet protocol networks cannot provide the service of selecting a secure path to transmit various types of data with specific security constraints. First, to solve the “secure path transmission” problem, this paper first proposes a search-transmit model for secure network path transmission, i.e., to find a multi-attribute optimized path that meets the specific security requirements in the search phase, and then transmit the packets along the optimized path in the transmission phase. Second, we propose a solution to the search-transmit model. The idea of the solution is to use the particle swarm optimization algorithm to search for a secure path that meets the multi-attribute requirements and then set the source route on the router to control the packet transmission. Finally, a prototype system based on network function virtualization is developed to evaluate the feasibility and performance of the proposed solution. Experimental results show that the proposed solution outperforms existing algorithms in terms of performance.


menu
Abstract
Full text
Outline
About this article

A Solution Supporting Secure Transmission of Big Data

Show Author's information Haonan Ling1( ), Yan Gao2, Huibin Wang3, Ming Chen1( )
Institute of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
China Electronic Systems Engineering Corporation, Beijing 100840, China
College of Computer and Information Engineering, Chuzhou University, Chuzhou 239000, China

Abstract

Traditional Internet protocol networks cannot provide the service of selecting a secure path to transmit various types of data with specific security constraints. First, to solve the “secure path transmission” problem, this paper first proposes a search-transmit model for secure network path transmission, i.e., to find a multi-attribute optimized path that meets the specific security requirements in the search phase, and then transmit the packets along the optimized path in the transmission phase. Second, we propose a solution to the search-transmit model. The idea of the solution is to use the particle swarm optimization algorithm to search for a secure path that meets the multi-attribute requirements and then set the source route on the router to control the packet transmission. Finally, a prototype system based on network function virtualization is developed to evaluate the feasibility and performance of the proposed solution. Experimental results show that the proposed solution outperforms existing algorithms in terms of performance.

Keywords: network function virtualization, segment routing, network secure path, search-transmit model, multi-attribute path optimization

References(21)

[1]
M. H. Haghighat and J. Li, Intrusion detection system using voting-based neural network, Tsinghua Science and Technology, vol. 26, no. 4, pp. 484–495, 2021.
DOI Google Scholar
[2]
H. Chen, Y. Zhang, Y. Cao, and J. Xie, Security issues and defensive approaches in deep learning frameworks, Tsinghua Science and Technology, vol. 26, no. 6, pp. 894–905, 2021.
DOI Google Scholar
[3]
P. Ma, B. Jiang, Z. Lu, N. Li, and Z. Jiang, Cybersecurity named entity recognition using bidirectional long short-term memory with conditional random fields, Tsinghua Science and Technology, vol. 26, no. 3, pp. 259–265, 2021.
DOI Google Scholar
[4]
P. L. Ventre, S. Salsano, M. Polverini, A. Cianfrani, A. Abdelsalam, C. Filsfils, P. Camarillo, and F. Clad, Segment routing: A comprehensive survey of research activities, standardization efforts, and implementation results, IEEE Communications Surveys & Tutorials, vol. 23, no. 1, pp. 182–221, 2021.
DOI Google Scholar
[5]
Z. Wang, H. Xing, T. Li, Y. Yang, R. Qu, and Y. Pan, A modified ant colony optimization algorithm for network coding resource minimization, IEEE Transactions on Evolutionary Computation, vol. 20, no. 3, pp. 325–342, 2016.
DOI Google Scholar
[6]
M. Farhanchi, R. Hassanzadeh, I. Mahdavi, and N. Mahdavi-Amiri, A modified ant colony system for finding the expected shortest path in networks with variable arc lengths and probabilistic nodes, Applied Soft Computing, vol. 21, pp. 491–500, 2014.
DOI Google Scholar
[7]
Y. Zheng, M. Han, L. He, Y. Li, G. Xing, and R. Hou, A QoS-supported multi-constrained routing strategy based on ant-colony optimization for named data networking, in Proc. of 2018 1st IEEE International Conference on Hot Information-Centric Networking (HotICN), Shenzhen, China, 2018, pp. 18–23.
DOI Google Scholar
[8]
S. K. Nivetha, R. Asokan, and N. Senthilkumaran, A swarm-based hybrid routing protocol to support multiple Quality of Service (QoS) metrics in mobile ad hoc networks, in Proc. of 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT), Tiruchengode, India, 2013, pp. 1–8.
DOI Google Scholar
[9]
Q. Fang, J. Han, L. Mao, and Z. Li, Exact and heuristic algorithm for multi-constrained optimal path problem, in Proc. 2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing, Sydney, Australia, 2011, pp. 45–51.
DOI Google Scholar
[10]
M. Puongmanee and T. Sanguankotchakorn, Multiple constraints QoS routing using priority metrics with control variables, in Proc. of 2013 19th IEEE International Conference on Networks (ICON), Singapore, 2013, pp. 1–6.
DOI Google Scholar
[11]
D. Shin, E. K. P. Chong, and H. J. Siegel, Multi-post path-based look ahead multi-constraint QoS routing, Journal of the Franklin Institute, vol. 349, no. 3, pp. 1106–1124, 2012.
DOI Google Scholar
[12]
D. Wang, D. Tan, and L. Liu, Particle swarm optimization algorithm: An overview, Soft Computing, vol. 22, no. 2, pp. 387–408, 2018.
DOI Google Scholar
[13]
K. Premalatha and A. M. Natarajan, Hybrid PSO and GA for global maximization, Int. J. Open Prob. Compt. Math., vol. 2, no. 4, pp. 597–608, 2009.
Google Scholar
[14]
Sumit and K. Ghai, Comparative analysis of improved path selection using PSO, International Journal of Computer Science and Network, vol. 6, no. 3, pp. 384–391 2017.
Google Scholar
[15]
Y. J. Gong, J. J. Li, Y. Zhou, Y. Li, H. S. H. Chung, Y. H. Shi, and J. Zhang, Genetic learning particle swarm optimization, IEEE Transactions on Cybernetics, vol. 46, no. 10, pp. 2277–2290, 2016.
DOI Google Scholar
[16]
X. Li and K. Yeung, Bandwidth-efficient network monitoring algorithms based on segment routing, Computer Networks, vol. 147, pp. 236–245, 2018.
DOI Google Scholar
[17]
G. Trimponias, Y. Xiao, X. Wu, H. Xu, and Y. Geng, Node-constrained traffic engineering: theory and applications, IEEE/ACM Transactions on Networking, vol. 27, no. 4, pp. 1344–1358, 2019.
DOI Google Scholar
[18]
A. Giorgetti, A. Sgambelluri, F. Paolucci, F. Cugini, and P. Castoldi, Segment routing for effective recovery and multidomain traffic engineering, Journal of Optical Communications and Networking, vol. 9, no. 2, pp. A223–A232, 2017.
DOI Google Scholar
[19]
F. Paolucci, Network service chaining using segment routing in multilayer networks, IEEE/OSA Journal of Optical Communications and Networking, vol. 10, no. 6, pp. 582–592, 2018.
DOI Google Scholar
[20]
D. Lebrun, Leveraging IPv6 segment routing for service function chaining, in Proc. CoNEXT Stud. Workshop, Heidelberg, German, 2015, pp. 1–15.
Google Scholar
[21]
A. Abdelsalam, P. L. Ventre, C. Scarpitta, A. Mayer, S. Salsano, P. Camarillo, and F. Clad, Clarence Filsfils SRPerf: A performance evaluation framework for IPv6 segment routing, IEEE Transactions on Network and Service Management, vol. 18, no. 2, pp. 2320–2333, 2021.
DOI Google Scholar
Publication history
Copyright
Acknowledgements
Rights and permissions

Publication history

Received: 12 November 2022
Accepted: 27 November 2022
Published: 19 May 2023
Issue date: October 2023

Copyright

© The author(s) 2023.

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 61772271).

Rights and permissions

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

Return