Information security model of block chain based on intrusion sensing in the IoT environment

  • Daming Li
  • Zhiming Cai
  • Lianbing Deng
  • Xiang Yao
  • Harry Haoxiang Wang
Article

Abstract

Block chain is a decentralized core architecture, which is widely used in emerging digital encryption currencies. It has attracted much attention and has been researched with the gradual acceptance of bitcoin. Block chaining technology has the characteristics of centralization, block data, no tampering and trust, so it is sought after by enterprises, especially financial institutions. This paper expounds the core technology principle of block chain technology, discusses the application of block chain technology, the existing regulatory problems and security problems, so as to provide some help for the related research of block chain technology. Intrusion detection is an important way to protect the security of information systems. It has become the focus of security research in recent years. This paper introduces the history and current situation of intrusion detection system, expounds the classification of intrusion detection system and the framework of general intrusion detection, and discusses all kinds of intrusion detection technology in detail. Intrusion detection technology is a kind of security technology to protect network resources from hacker attack. IDS is a useful supplement to the firewall, which can help the network system to quickly detect attacks and improve the integrity of the information security infrastructure. In this paper, intrusion detection technology is applied to block chain information security model, and the results show that proposed model has higher detection efficiency and fault tolerance.

Keywords

Internet of things Intrusion detection Block chain Information security Model building Technical analysis 

Notes

Acknowledgements

This research is financially supported by the Project of Macau Foundation (No. M1617): The First-phase Construction of Big-Data on Smart Macao.

References

  1. 1.
    Zheng, X., Ge, B.: The evolution trend of information management of supply chain in China under the information environment. Inf. Sci. 10, 128–133 (2016)Google Scholar
  2. 2.
    Nakamoto S. Bitcoin: a peer-to-peer electronic cash system[EB/OL]. oGoogle Scholar
  3. 3.
    Ping, Z., Yu, D., Bin, L.: Chinese Block Chain Technology and Application Development White Paper. Ministry of Industry and Information Technology, Beijing (2016)Google Scholar
  4. 4.
    Swan, M.: Blockchain: Blueprint for a New Economy. O’Reilly Media Inc, Sebastopol (2015)Google Scholar
  5. 5.
    Zhao, H., Li, X.F., Zhan, L.K., et al.: Data integrity protection method for icroorganism sampling robots based on blockchain technology. J. Huazhong Univ. Sci. Technol. 43(Z1), 216–219 (2015)Google Scholar
  6. 6.
    Swan, M.: Block chain thinking: the brain as a decentralized auto nomous corporation. IEEE Technol. Soc. Mag. 34(4), 41–52 (2015)CrossRefGoogle Scholar
  7. 7.
    Godsiff, P.: Bitcoin: bubble or blockchain. In: The 9th KES International Conference on Agent and Multi-Agent Systems: Technologies and Applications (KESAMSTA), vol. 38, pp. 191–203 (2015)Google Scholar
  8. 8.
    Wilson, D., Ateniese, G.: From pretty good to great: enhancing PGP using Bitcoin and the blockchain. In: The 9th International Conference on Network and System Security, New York, pp. 358–379 (2015)Google Scholar
  9. 9.
    Kypriotaki, K.N., Zamani, E.D., Giaglis, G.M.: From Bitcoin to decentralized autonomous corporations: extending the application scope of decentralized peer-to-peer networks and block chains. In: The 17th International Conference on Enterprise Information Systems (ICEIS2015), pp. 280–290 (2015)Google Scholar
  10. 10.
    Atzori, L., Iera, A., Morabito, G.: The internet of things: a survey. Comput. Netw. 54(15), 2787–2805 (2010)CrossRefMATHGoogle Scholar
  11. 11.
    President’s Council of Advisors on Science and Technology. Leadership Under Challenge. Information Technology R&D in a Competitive World, An Assessment of the Federal Networking and Information Technology Program[EB/OL] (2017). https://www.ostpgov/pdf/nitrd_review.pdf
  12. 12.
    International Telecommunication Union. ITU Internet Reports 2005: The Internet of Things (2005)Google Scholar
  13. 13.
    Petrovic, D., Shah, R.C., Ramchandran, K.: Data funneling: routing with aggregation and compression for wireless sensor networks. In: Proceedings of the 1st IEEE International Workshop on Sensor Network Protocols and Applications (SNPA’03). Seattle, USA, pp. 140–168 (2003)Google Scholar
  14. 14.
    Yuan, Y., Kam, M.: Distributed decision fusion with a random access channel for sensor network applications. IEEE Trans. Instrum. Meas. 53(4), 1239–1320 (2004)CrossRefGoogle Scholar
  15. 15.
    Tan, H., Korpeoglu, I.: Power efficient data gathering and aggregation in wireless sensor networks. ACM SIGMOD Record 32(4), 50–89 (2003)CrossRefGoogle Scholar
  16. 16.
    Anderson, J.P. Computer security threat monitoring and surveillance. Technical Report, James P Anderson Co., Fort Washington, Pennsylvania (1980)Google Scholar
  17. 17.
    Denning, D.E.: An intrusion -detection model. IEEE Trans. Softw. Eng. 13(2), 220–235 (1987)Google Scholar
  18. 18.
    Aurobindo, S.: An introduction to intrusion detection. ACM Crossorads 2(4), 3–7 (1996). http://www.acm.org/crossroads/xrds2-4/intrus.html
  19. 19.
    Chen, Q., Zhang, G., Yang, X., et al.: Single image shadow detection and removal based on feature fusion and multiple dictionary learning. Multimed. Tools Appl. (2017).  https://doi.org/10.1007/s11042-017-5299-0 Google Scholar
  20. 20.
    Desai, A.S., Gaikwad, D.P.: Real time hybrid intrusion detection system using signature matching algorithm and fuzzy-GA. In: 2016 IEEE International Conference on Advances in Electronics, Communication and Computer Technology (ICAECCT), pp. 291–294 (2016)Google Scholar
  21. 21.
    Aburomman, A.A., Reaz, M.B.I.: A novel SVM-kNN-PSO ensemble method for intrusion detection system. Appl. Soft Comput. 38, 360–372 (2016)CrossRefGoogle Scholar
  22. 22.
    Zhang, Y., Wang, H., Xie, Y.: An intelligent hybrid model for power flow optimization in the cloud-IOT electrical distribution network. Clust. Comput. (2017).  https://doi.org/10.1007/s10586-017-1270-0 Google Scholar
  23. 23.
    Anwar, S., Mohamad Zain, J., Zolkipli, M.F., Inayat, Z., Khan, S., Anthony, B., Chang, V.: From intrusion detection to an intrusion response system: fundamentals, requirements, and future directions. Algorithms 10(2), 39 (2017)CrossRefGoogle Scholar
  24. 24.
    Haider, W., Hu, J., Slay, J., Turnbull, B.P., Xie, Y.: Generating realistic intrusion detection system dataset based on fuzzy qualitative modeling. J. Netw. Comput. Appl. 87, 185–192 (2017)CrossRefGoogle Scholar
  25. 25.
    Sedjelmaci, H., Senouci, S.M., Ansari, N.: Intrusion detection and ejection framework against lethal attacks in UAV-aided networks: a Bayesian game-theoretic methodology. IEEE Trans. Intell. Transp. Syst. 18(5), 1143–1153 (2017)CrossRefGoogle Scholar
  26. 26.
    Cai, Z., Deng, L., Li, D., et al.: A FCM cluster: cloud networking model for intelligent transportation in the city of Macau. Clust. Comput. (2017).  https://doi.org/10.1007/s10586-017-1216-6 Google Scholar
  27. 27.
    Bostani, H., Sheikhan, M.: Modification of supervised OPF-based intrusion detection systems using unsupervised learning and social network concept. Pattern Recogn. 62, 56–72 (2017)CrossRefGoogle Scholar
  28. 28.
    Zhang, S., Wang, H., Huang, W.: Two-stage plant species recognition by local mean clustering and weighted sparse representation classification. Clust. Comput. 20, 1517 (2017).  https://doi.org/10.1007/s10586-017-0859-7 CrossRefGoogle Scholar
  29. 29.
    Wang, H., Wang, J.: An effective image representation method using kernel classification. In: IEEE 26th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 853–858 (2014)Google Scholar
  30. 30.
    Nair, R., Nayak, C., Watkins, L., Fairbanks, K.D., Memon, K., Wang, P., Robinson, W.H.: The resource usage viewpoint of industrial control system security: an inference-based intrusion detection system. In: Cybersecurity for Industry 4.0, pp. 195–223. Springer, New York (2017)Google Scholar
  31. 31.
    Dhillon, H.S., Huang, H., Viswanathan, H.: Wide-area wireless communication challenges for the Internet of Things. IEEE Commun. Mag. 55(2), 168–174 (2017)CrossRefGoogle Scholar
  32. 32.
    Pramudianto, F., Eisenhauer, M., Kamienski, C.A., Sadok, D. and Souto, E.J.: Connecting the internet of things rapidly through a model driven approach. In: IEEE 3rd World Forum on Internet of Things (WF-IoT), pp. 135–140 (2016)Google Scholar
  33. 33.
    Deng, L., Li, D., Yao, X., Cox, D., Wang, H.: Mobile network intrusion detection for IoT system based on transfer learning algorithm. Clust. Comput. 1–16 (2018)Google Scholar
  34. 34.
    Li, D., Deng, L., Gupta, B.B., Wang, H., Choi, C.: A novel CNN based security guaranteed image watermarking generation scenario for smart city applications. Inf. Sci. (2018)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Daming Li
    • 1
    • 2
    • 3
  • Zhiming Cai
    • 4
  • Lianbing Deng
    • 5
    • 6
  • Xiang Yao
    • 6
  • Harry Haoxiang Wang
    • 7
    • 8
  1. 1.The Post-Doctoral Research Center of Zhuhai Da Hengqin Science and Technology Development Co., Ltd.HengqinChina
  2. 2.City University of MacauMacauChina
  3. 3.International Postdoctoral Science and Technology Research Institute Co., Ltd.WuhanChina
  4. 4.Macau Big Data Research Centre for Urban GovernanceCity University of MacaoMacauChina
  5. 5.Huazhong University of Science and TechnologyWuhanChina
  6. 6.Zhuhai Da Hengqin Science and Technology Development Co., Ltd.HengqinChina
  7. 7.Cornell UniversityIthacaUSA
  8. 8.GoPerception LaboratoryNew YorkUSA

Personalised recommendations