Skip to main content

Research on Security Vulnerabilities Based on Artificial Intelligence

  • Conference paper
  • First Online:
  • 1587 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11643))

Abstract

Security vulnerability research is the core content of information security research. Faced with the increasing scale of software, security vulnerabilities have brought unprecedented severe challenges, artificial methods have been unable to meet the demand of the research. How to apply artificial intelligence technology such as machine learning and natural language processing to security vulnerability research has become an urgent issue. This paper summarizes the common research methods of vulnerability, expounds the key technology of intelligent vulnerability research, points out that intelligent vulnerability mining is the focus of research on security vulnerability based on artificial intelligence, analyzes and summarizes the latest research results in related fields in recent years, puts forward the existing problems, and gives the corresponding solutions.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
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

Learn about institutional subscriptions

References

  1. Zhang, Y.Q., Gong, Y.F., Wang, H.: Vulnerability identification and description specification. National Information Security Standardization Technical Committee

    Google Scholar 

  2. Mell, P., Scarfone, K., Romanosky, S.: Common vulnerability scoring system. IEEE Secur. Priv. 4(6), 85–95 (2006)

    Article  Google Scholar 

  3. Chowdhury, I., Zulkernine, M.: Using complexity, coupling and cohesion metrics as early indicators of vulnerabilities. J. Syst. Arch. 57(3), 294–313 (2011)

    Article  Google Scholar 

  4. Chowdhury, I., Zulkernine, M.: Can complexity, coupling and cohesion metrics be used as early indicators of vulnerabilities, pp. 1963–1969 (2010)

    Google Scholar 

  5. Meng, Q., Wen, S., Zhang, B.: Automatically discover vulnerability through similar functions, pp. 3657–3661 (2016)

    Google Scholar 

  6. Medeiros, I., Neves, N., Correia, M.: Detecting and removing web application vulnerabilities with static analysis and data mining. IEEE Trans. Reliab. 65(1), 54–69 (2016)

    Article  Google Scholar 

  7. Yamaguchi, F., Maier, A., Gascon, H.: Automatic inference of search patterns for taint-style vulnerabilities. In: 2015 IEEE Symposium on Security and Privacy (SP), pp. 797–812 (2015)

    Google Scholar 

  8. Wang, D., Lin, M., Zhang, H.: Detect related bugs from source code using bug information. Computer Software and Applications Conference (COMPSAC), pp. 228–237 (2010)

    Google Scholar 

  9. Yamaguchi, F., Lottmann, M., Rieck, K.: Generalized vulnerability extrapolation using abstract syntax trees. In: The 28th Annual Computer Security Applications Conference, pp. 359–368 (2012)

    Google Scholar 

  10. Yamaguchi, F., Wressnegger, C., Gascon, H.: Chucky: exposing missing checks in source code for vulnerability discovery. In: The 2013 ACM SIGSAC Conference on Computer & Communications Security, pp. 499–510 (2013)

    Google Scholar 

  11. Meng, Q., Wen, S., Zhang, B.: Automatically discover vulnerability through similar functions. In: Progress in Electromagnetic Research Symposium (PIERS), pp. 3657–3661 (2016)

    Google Scholar 

  12. Meng, Q., Zhang, B., Feng, C.: Detecting buffer boundary violations based on SVM. In: 3rd International Conference on Information Science and Control Engineering (ICISCE), pp. 313–316 (2016)

    Google Scholar 

  13. Heo, K., Oh, H., Yi, K.: Machine-learning-guided selectively unsound static analysis. In: The 39th International Conference on Software Engineering, pp. 519–529 (2017)

    Google Scholar 

  14. Grieco, G., Grinblat, G.L., Uzal, L.: Toward large-scale vulnerability discovery using machine learning. In: The Sixth ACM Conference on Data and Application Security and Privacy, pp. 85–96 (2016)

    Google Scholar 

  15. Godefroid, P., Peleg, H., Singh, R.: Learn&Fuzz: machine learning for input fuzzing. In: The 32nd IEEE/ACM International Conference on Automated Software Engineering, pp. 50–59 (2017)

    Google Scholar 

  16. Pang, Y., Xue, X., Wang, H.: Predicting vulnerable software components through deep neural network. In: The 2017 International Conference on Deep Learning Technologies, pp. 6–10. (2017)

    Google Scholar 

  17. Wu, F., Wang, J., Liu, J.: Vulnerability detection with deep learning. In: 3rd IEEE International Conference on Computer and Communications (ICCC), pp. 1298–1302 (2017)

    Google Scholar 

  18. Li, Z., Zou, D., Xu, S.: VulDeePecker: a deep learning-based system for vulnerability detection (2018)

    Google Scholar 

  19. Younis, A., Malaiya, Y., Anderson, C.: To fear or not to fear that is the question: code characteristics of a vulnerable function with an existing exploit. In: The Sixth ACM Conference on Data and Application Security and Privacy, pp. 97–104 (2016)

    Google Scholar 

  20. Allodi, L., Massacci, F.: A preliminary analysis of vulnerability scores for attacks in wild: the EKITS and SYM datasets. In: The 2012 ACM Workshop on Building Analysis Datasets and Gathering Experience Returns for Security, pp. 17–24 (2012)

    Google Scholar 

  21. Shin, Y., Meneely, A., Williams, L.: Evaluating complexity, code churn and developer activity metrics as indicators of software vulnerabilities. IEEE Trans. Softw. Eng. 37(6), 772–787 (2011)

    Article  Google Scholar 

  22. Ben, O.L., Chehrazi, G., Bodden, E.: Factors impacting the effort required to fix security vulnerabilities. In: International Information Security Conference, pp. 102–119 (2015)

    Google Scholar 

Download references

Acknowledgement

This paper is supported by Hubei Provincial Education Department of Scientific Research Project of B2017420.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Qian Zhu .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhu, Q., Liang, L. (2019). Research on Security Vulnerabilities Based on Artificial Intelligence. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11643. Springer, Cham. https://doi.org/10.1007/978-3-030-26763-6_37

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-26763-6_37

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26762-9

  • Online ISBN: 978-3-030-26763-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics