Skip to main content
SpringerLink
Log in

Mining Network Security Holes Based on Data Flow Analysis in Smart Grid

  • Conference paper
  • First Online:
Smart Grid and Internet of Things (SGIoT 2019)

Included in the following conference series:

  • 318 Accesses

Abstract

With the popularity of mobile terminals and the sharp increase in network data traffic, the problem of security loopholes has become increasingly prominent. The traditional vulnerability detection methods can no longer meet the demands for detection efficiency. In order to satisfy the high requirements on network security in the era of big data, the vulnerability mining technology is extremely urgent. This paper describes the current situation and introduces relevant security technology and algorithm in smart grid. The decision tree algorithm is selected as the basic algorithm of big data security technology. Through the test, the missing alarm rate and false alarm rate are simulated experimentally. We obtain the results of experiments by controlling variables, which proves that our algorithm can effectively detect IP scanning, Port scanning and other attacks.

Supported by State Grid Xinjiang Electric Power Co., Ltd.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 54.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 69.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Hu, R.: Key technology for big visual data analysis in security space and its applications. In: 2016 International Conference on Advanced Cloud and Big Data (CBD), vol. 3, no. 4, p. 333 (2016)

    Google Scholar 

  2. Yu, J., Wang, K., Li, P., Xia, R., Guo, S., Guo, M.: Efficient trustworthiness management for malicious user detection in big data collection. In: IEEE Transactions on Big Data, October 2017

    Google Scholar 

  3. Albu, A.: From logical inference to decision trees in medical diagnosis. In: E-Health and Bioengineering Conference (EHB). Sinaia 2017, 65–68 (2017)

    Google Scholar 

  4. Park, K.: On the effectiveness of route-based packet filtering for distributed DoS attack prevention in power-law internets. In: 2018 ACM SIGCOMM Computer Communication Review, December 2018

    Google Scholar 

  5. laski, J., Stanley, W.: Software verification and analysis. In: 2018 2nd IEEE Conference on Energy Internet and Energy System Integration (EI2), Springer, London (2018) https://doi.org/10.1007/978-1-84882-240-5

  6. Wang, K., et al.: Wireless big data computing in smart grid. IEEE Wireless Communi. 24(2), 58–64 (2017)

    Article  Google Scholar 

  7. Wang, K., Du, M., Maharjan, S., Sun, Y.: Strategic honeypot game model for distributed denial of service attacks in the smart grid. IEEE Trans. Smart Grid 8(5), 2474–2482 (2017)

    Article  Google Scholar 

  8. Xu, C., Wang, K., Li, P., Xia, R., Guo, S., Guo, M.: Renewable energy-aware big data analytics in geo-distributed data centers with reinforcement learning. IEEE Tran. Network Sci. Eng. 7, 205–215 (2018)

    Article  Google Scholar 

  9. Amir, M.A.U.: Optimal specification of wave flume in confined space. In: 2016 International Conference on Information and Communication Technology (ICICTM), Kuala Lumpur, pp. 136–140 (2016)

    Google Scholar 

  10. Du, M., Wang, K., Xia, Z., Zhang, Y.: Differential privacy preserving of training model in wireless big data with edge computing. In: IEEE Transactions on Big Data, May 2018

    Google Scholar 

  11. Al-Shomrani, A., Yang, Y.M., Wang, X.: Quickest detection of false data injection attack in wide-area smart grids. IEEE Trans. Smart Grid 6(6), 2725–2735 (2017)

    Google Scholar 

  12. Wang, K., Fathy, F., Jambi, K.: Policy enforcement for big data security. In: 2017 2nd International Conference on Anti-Cyber Crimes (ICACC), pp. 70–74. Abha (2017)

    Google Scholar 

  13. Burguera, I., Zurutuza, U.: Behavior-based Malware detection system for Android. In: Proceedings of the 1st ACM Workshop on Security and Privacy in Smartphones and Mobile Devices, pp. 15–26. ACM, Chicago (2011)

    Google Scholar 

  14. Xie, L., Zhang, X., Seifert, J.: A behavior-based malware detection system for cellphone devices. In: Proceedings of the Third ACM Conference on Wireless Network Security, pp. 37–48. New Jersey, ACM (2010)

    Google Scholar 

  15. Gavankar, S.S., Sawarkar, S.D.: Eager decision tree. In: 2nd International Conference for Convergence in Technology (I2CT). Mumbai 2017, 837–840 (2017)

    Google Scholar 

  16. Wang, K., Ouyang, Z., Krishnan, R., Shu, L., He, L.: A game theory based energy management system using price elasticity for smart grids. IEEE Trans. Ind. Inform. 11(6), 1607–1616 (2015)

    Article  Google Scholar 

  17. Al-Hoqani, W.M., Giampapa, J.A.: Difficulties of marking decision tree diagrams. Computing Conference. London 2017, 1190–1194 (2017)

    Google Scholar 

  18. Wang, Y., Wang, K., Huang, H., Miyazaki, T., Guo, S.: Traffic and computation co-offloading with reinforcement learning in fog computing for industrial applications. IEEE Trans. Ind. Inform. 15(2), 976–986 (2019)

    Article  Google Scholar 

  19. Ignatov, D., Ignatov, A.: Decision stream: cultivating deep decision trees. In: 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI), Boston, MA, pp. 905–912 (2017)

    Google Scholar 

  20. Gao, M., Wang, K., He, L.: Probabilistic model checking and scheduling implementation of energy router system in energy internet for green cities. IEEE Trans. Ind. Inform. 14(4), 1501–1510 (2018)

    Article  Google Scholar 

  21. Kumar, R.: Development of synchronization system of two spark gaps. In: 2012 IEEE 5th India International Conference on Power Electronics (IICPE), Delhi, pp. 1–3 (2012)

    Google Scholar 

  22. Revathy, P., Mukesh, R.: Analysis of big data security practices. In: 2017 3rd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), Tumkur, pp. 264–267 (2017)

    Google Scholar 

  23. Wang, K., Shao, Y., Xie, L., Wu, J., Guo, S.: Adaptive and fault-tolerant data processing in healthcare IoT based on fog computing. IEEE Transactions on Network Science and Engineering, July 2018

    Google Scholar 

  24. Lighari, S.N., Hussain, D.M.A.: Hybrid model of rule based and clustering analysis for big data security. In: 2017 First International Conference on Latest trends in Electrical Engineering and Computing Technologies (INTELLECT), Karachi, pp. 1–5 (2017)

    Google Scholar 

  25. Aljuhani, A., Alharbi, T.: Virtualized network functions security attacks and vulnerabilities. In: IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC). Las Vegas, NV 2017, 1–4 (2017)

    Google Scholar 

  26. Wang, K., Yu, J., Liu, X., Guo, S.: A pre-authentication approach to proxy reencryption in big data context. In: IEEE Transactions on Big Data, May 2017

    Google Scholar 

  27. Li, P., Min, X.: Accurate marking method of network attacking information based on big data analysis. In: 2019 International Conference on Intelligent Transportation, Big Data Smart City (ICITBS), Changsha, China, pp. 228–231 (2019)

    Google Scholar 

  28. Wang, K., Li, H., Maharjan, S., Zhang, Y., Guo, S.: Green energy scheduling for demand side management in the smart grid. IEEE Trans. Green Commun. Network. 2(2), 596–611 (2018)

    Article  Google Scholar 

  29. Nethercote, N., Seward, J.: A Framework for Heavyweight Dynamic Binary Instrumentation (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. State Grid Xinjiang Electric Power Research Institute, Xinjiang, China

    Yang Li

  2. State Grid Xinjiang Electric Power Co., Ltd., Xinjiang, China

    Xiaohua Liu, Lixin Zhang & Wenbin Guo

  3. Global Energy Internet Research Institute Ltd., Nanjing, China

    Qian Guo

Authors
  1. Yang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaohua Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lixin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wenbin Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Qian Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yang Li .

Editor information

Editors and Affiliations

  1. National Changhua University of Education, Changhua City, Taiwan

    Der-Jiunn Deng

  2. National Taiwan Normal University, Taipei, Taiwan

    Ai-Chun Pang

  3. Department of Industrial Engineering and Management, National Chiao Tung University, Hsinchu, Taiwan

    Chun-Cheng Lin

Rights and permissions

Reprints and permissions

Copyright information

© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Li, Y., Liu, X., Zhang, L., Guo, W., Guo, Q. (2020). Mining Network Security Holes Based on Data Flow Analysis in Smart Grid. In: Deng, DJ., Pang, AC., Lin, CC. (eds) Smart Grid and Internet of Things. SGIoT 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 324. Springer, Cham. https://doi.org/10.1007/978-3-030-49610-4_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-49610-4_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-49609-8

  • Online ISBN: 978-3-030-49610-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation