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A Blockchain-Based Security Traffic Measurement Approach to Software Defined Networking

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Abstract

Software Defined Networking (SDN) architecture separates control plane and data plane, making network flexible and programmable. Since the large number of devices connected to the Internet of things (IoT) networks, the SDN-based network architecture makes the deployment and configuration of IoT much easier. In the IoT network, the fine-grained network traffic is critical to network management, then we propose a novel scheme to measure the fine-grained network traffic in the SDN-based IoT networks. In SDN-based IoT networks, the controller is very easy to be attacked, we introduce the blockchain technology into the measurement framework to ensure the security and consistency of the statistics. To measure flow traffic with low overhead and high accuracy, we collect the statistics of coarse-grained traffic of flows and fine-grained traffic of links, and model the network traffic as an ARIMA model and forecast the network traffic with the coarse-grained measurement of flows. Then, we propose an objective function to decrease the estimation errors. Due to the objective function is an NP-hard problem, we present a heuristic algorithm to obtain the optimal solution of the fine-grained measurement. Finally, we conduct some simulations to verify the validity of the proposed measurement scheme. Simulation results show that our approach is feasible and effective.

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References

  1. Memos VA, Psannis KE, Ishibashi Y et al (2018) An efficient algorithm for media-based surveillance system (EAMSuS) in IoT smart city framework. Futur Gener Comput Syst 83(4):619–628

    Article  Google Scholar 

  2. Hossain MS, Muhammad G, Abdul W et al (2018) Cloud-assisted secure video transmission and sharing framework for smart cities. Futur Gener Comput Syst 83:596–606

    Article  Google Scholar 

  3. Ali I, Gani A, Ahmedy I et al (2018) Data collection in smart communities using sensor cloud: recent advances, taxonomy, and future research directions. IEEE Commun Mag 56(7):192–197

    Article  Google Scholar 

  4. Suarez-Varela J, Barlet-Ros P (2017) Towards a NetFlow implementation for OpenFlow software-defined networks. In: Proc ITC’17 1:187–195

  5. Huang L, Zhi X, Gao Q et al (2016) Design and implementation of multicast routing system over SDN and sFlow. Proc ICCSN’16:524–529

  6. Zhang X, Zhu Q (2018) Hierarchical caching for statistical QoS guaranteed multimedia transmissions over 5G cloud computing mobile wireless networks. IEEE Wirel Commun 25(3):12–20

    Article  Google Scholar 

  7. Xu H, Yu Z, Qian C et al (2017) Minimizing flow statistics collection cost of SDN using wildcard requests. Proc INFOCOM’17, p 1–9

  8. Sharma PK, Singh S, Jeong YS et al (2017) Distblocknet: a distributed blockchains-based secure sdn architecture for iot networks. IEEE Commun Mag 55(9):78–85

    Article  Google Scholar 

  9. Xiong Z, Zhang Y, Niyato D et al (2018) When mobile blockchain meets edge computing. IEEE Commun Mag 56(8):33–39

    Article  Google Scholar 

  10. Alphand O, Amoretti M, Claeys T et al (2018) IoTChain: a blockchain security architecture for the Internet of Things. In: Proceedings of WCNC’18, p 1–6

  11. Unnikrishnan J, Suresh KK (2016) Modelling the impact of government policies on import on domestic price of Indian gold using ARIMA intervention method. Int J Math Math Sci 2016:1–6

    Article  MathSciNet  Google Scholar 

  12. Jiang DD, Huo LW, Li Y (2018) Fine-granularity inference and estimations to network traffic for SDN. PLoS One 13(5):1–23

    Google Scholar 

  13. The Mininet Platform. http://mininet.org/. Accessed Dec 2018

  14. The Ryu Platform. https://github.com/osrg/ryu/. Accessed Dec 2018

  15. He Q, Wang X, Huang M (2018) OpenFlow-based low-overhead and high-accuracy SDN measurement framework. Trans Emerg Telecommun Technol 29(2):1–17

    Google Scholar 

  16. Roughan M, Zhang Y, Willinger W et al (2012) Spatio-temporal compressive sensing and internet traffic matrices. IEEE/ACM Trans Netw 20(3):662–676

    Article  Google Scholar 

  17. Baktir AC, Ozgovde A, Ersoy C (2017) How can cloud computing benefit from software-defined networking: a survey, use cases, and future directions. IEEE Commun Surv Tutor 19(4):2359–2391

    Article  Google Scholar 

  18. Liu C, Malboubi C, Chuah CN (2016) OpenMeasure: adaptive flow measurement and inference with online learning in SDN. In Proceedings of INFOCOM’16, p 47–52

  19. Shu ZG, Wan JF, Wang SY et al (2016) Traffic engineering in software-defined networking: measurement and management. IEEE Access 4:3246–3256

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by National Natural Science Foundation of China (No. 61571104), Sichuan Science and Technology Program (No. 2018JY0539), Key projects of the Sichuan Provincial Education Department (No. 18ZA0219), Fundamental Research Funds for the Central Universities (No. ZYGX2017KYQD170), and Innovation Funding (No. 2018510007000134). The authors wish to thank the reviewers for their helpful comments. Dr. Dingde Jiang is corresponding author of this paper (email: jiangdd99@sina.com).

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Authors and Affiliations

  1. College of Computer Science and Engineering, Northeastern University, Shenyang, 110819, China

    Liuwei Huo

  2. School of Astronautics and Aeronautics, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Dingde Jiang & Sheng Qi

  3. Mechatronics Engineering, Middle Tennessee State University, Murfreesboro, TN, 37132, USA

    Lei Miao

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  1. Liuwei Huo
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  2. Dingde Jiang
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  3. Sheng Qi
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  4. Lei Miao
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Correspondence to Dingde Jiang.

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Huo, L., Jiang, D., Qi, S. et al. A Blockchain-Based Security Traffic Measurement Approach to Software Defined Networking. Mobile Netw Appl 26, 586–596 (2021). https://doi.org/10.1007/s11036-019-01420-6

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