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Network attack prediction method based on threat intelligence for IoT

  • Hongbin Zhang
  • Yuzi Yi
  • Junshe Wang
  • Ning Cao
  • Qiang Duan
Article
  • 89 Downloads

Abstract

The Social Internet of Things (SIoT) is a combination of the Internet of Things (IoT) and social networks, which enables better service discovery and improves the user experience. The threat posed by the malicious behavior of social network accounts also affects the SIoT, this paper studies the analysis and prediction of malicious behavior for SIoT accounts, proposed a method for predicting malicious behavior of SIoT accounts based on threat intelligence. The method uses support vector machine (SVM) to obtain threat intelligence related to malicious behavior of target accounts, analyze contextual data in threat intelligence to predict the behavior of malicious accounts. By collecting and analyzing the data in a SIoT environment, verifies the malicious behavior prediction method of SIoT account proposed in this paper.

Keywords

Social internet of things Internet of things Support vector machine Threat intelligence Social networks Malicious behavior 

Notes

Acknowledgements

This research was supported by the National Natural Science Foundation of China (61672206,61572170), Hebei Province Science and Technology Support Program (17210104D), Hebei Province Innovation Capacity Improvement Program Soft Science Research and Science Popularization Project (17K50702D), College Science and Technology Research Project of Heibei Province (ZD2015099). Yuzi Yi is the corresponding author of this article.

References

  1. 1.
    Atzori L, Iera A, Morabito G (2011) SIOt: giving a social structure to the internet of things. IEEE Commun Lett 15(11):1193–1195CrossRefGoogle Scholar
  2. 2.
    Bao-Tong M, Xun L, Shu-Sen Z (2018) A survey on SIoT. Online Publishing 41Google Scholar
  3. 3.
    Boshmaf Y, Muslukhov I, Beznosov K et al (2011) The socialbot network: when bots socialize for fame and money. In: 27th computer security applications conference, pp 93–102Google Scholar
  4. 4.
    Douceur J R (2002) The sybil attack international workshop on peer-to-peer systems. Springer, Berlin, pp 251–260CrossRefGoogle Scholar
  5. 5.
    Gao H, Hu J, Wilson C et al (2010) Detecting and characterizing social spam campaigns. Duke University, Durham, pp 681–683Google Scholar
  6. 6.
    Gartner (2018) Definition: threat intelligence. https://www.gartner.com/doc/2487216/definition-threat-intelligence.Gartner. Accessed 05 June 2018
  7. 7.
    Guinard D, Trifa V (2009) Towards the web of things: web mashups for embedded devicesGoogle Scholar
  8. 8.
    Guinard D, Fischer M, Trifa V (2010) Sharing using social networks in a composable web of things. IEEE International Conference on Pervasive Computing and Communications Workshops. IEEE 2010:702–707Google Scholar
  9. 9.
    Guo R, Wang H, Zhong L et al (2014) Harbinger: an analyzing and predicting system for online social network users’ behavior. In: International conference on database systems for advanced applications. Springer International Publishing, pp 531–534Google Scholar
  10. 10.
    Liu W, Luo X, Liu Y, Liu J, Liu M, Shi YQ (2018) Localization algorithm of indoor Wi-Fi access points based on signal strength relative relationship and region division. Computers, Materials & Continua 55(1):071–093Google Scholar
  11. 11.
    Tran N, Li J, Subramanian L et al (2011) Optimal Sybil-resilient node admission control. In: IEEE INFOCOM. IEEE, pp 3218–3226Google Scholar
  12. 12.
    Vapnik Vladimir N (1995) The nature of statistical learning theory. Technometrics 38(4):409–409zbMATHGoogle Scholar
  13. 13.
    Wang G, Konolige T, Wilson C et al (2013) You are how you click: clickstream analysis for Sybil detection. In: Usenix conference on security, pp 241–256Google Scholar
  14. 14.
    Wu C, Zapevalova E, Chen Y, Li F (2018) Time optimization of multiple knowledge transfers in the big data environment. Comput Mater Continua 54(3):269–285Google Scholar
  15. 15.
    Zangerle E, Specht G (2014) “sorry, I was hacked” a classification of compromised Twitter accounts. In: Proceedings of the 29th annual ACMS syposium on applied computing. Gyeongju, Korea, pp 587–593Google Scholar

Copyright information

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

Authors and Affiliations

  1. 1.School of Information Science and EngineeringHebei University of Science and TechnologyShijiazhuangPeople’s Republic of China
  2. 2.Hebei Key Laboratory of Network and Information SecurityHebei Normal UniversityShijiazhuangChina
  3. 3.College of Information EngineeringQingdao Binhai UniversityQingdaoPeople’s Republic of China
  4. 4.Department of Information Science & TechnologyPennsylvania State UniversityAbingtonUSA

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