Skip to main content
SpringerLink
Log in
Book cover

International Conference on Ubiquitous Information Technologies and Applications

International Conference on Computer Science and its Applications

CUTE 2017, CSA 2017: Advances in Computer Science and Ubiquitous Computing pp 846–850Cite as

Data Stream Clustering Algorithm Based on Bucket Density for Intrusion Detection

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 474))

Abstract

The ability to process data streams has become one of the challenges of the current intrusion detection systems. A data stream clustering algorithm based on bucket density is proposed for this situation which is able to identify clusters in any shapes and the speed of online layer is fast. Feedback principle is used to solve the problem that some of the edge of the bucket is lost and users does not need to specify the number of clusters. An intrusion detection system is constructed with the improved algorithm. The experiment shows that the algorithm proposed has fast speed for clustering. The system based on the algorithm has a better capability of detection.

This is a preview of subscription content, 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   259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   329.99
Price excludes VAT (USA)
  • Durable hardcover 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. Guo, C., Zhou, Y.J., Ping, Y., Zhang, Z.K., Liu, G.L., Yang, Y.X.: A distance sun-based hybrid method for intrusion detection. Appl. Intell. 40(1), 178–188 (2014)

    Article  Google Scholar 

  2. Yazdan, J., Hossein, N.: A lattice based nearest neighbor classifier for anomaly intrusion detection. J. Adv. Comput. Res. 4(4), 51–60 (2013)

    Google Scholar 

  3. Li, Y.H., Li, D.Y., Cui, M.T., Li, H.: Real-time detection framework of network intrusion based on data flow. J. Comput. Appl. 35(2), 416–419 (2015)

    Google Scholar 

  4. Cassisi, C., Ferro, A., Giugno, R., et al.: Enhancing density-based clustering: parameter reduction and outlier detection. Inf. Syst. 38(3), 317–330 (2013)

    Article  Google Scholar 

  5. Aggarwal, C.C., Han, J.W., Wang, J.Y., et al.: A framework for clustering evolving data streams. In: Proceedings of the 29th International Conference on Very Large Data Bases, VLDB Endowment, Berlin, pp. 81–92 (2003)

    Google Scholar 

Download references

Acknowledgments

This work was funded by the National Natural Science Foundation of China (61772282, 61373134, and 61402234). It was also supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX17_0901) and Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET). We declare that we do not have any conflicts of interest to this work.

Author information

Authors and Affiliations

  1. School of Computer and Software, Jiangsu Engineering Center of Network Monitoring, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, China

    Chunyong Yin & Lian Xia

  2. College of Information Engineering, Yangzhou University, Yangzhou, China

    Jin Wang

Authors
  1. Chunyong Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lian Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chunyong Yin .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Seoul University of Science and Technology, Seoul, Korea (Republic of)

    James J. Park

  2. Department of Business Science, University of Salerno, Salerno, Italy

    Vincenzo Loia

  3. Department of Multimedia Engineering, Dongguk University, Seoul, Soul-t’ukpyolsi, Korea (Republic of)

    Gangman Yi

  4. Department of Multimedia Engineering, Dongguk University, Seoul, Soul-t’ukpyolsi, Korea (Republic of)

    Yunsick Sung

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Yin, C., Xia, L., Wang, J. (2018). Data Stream Clustering Algorithm Based on Bucket Density for Intrusion Detection. In: Park, J., Loia, V., Yi, G., Sung, Y. (eds) Advances in Computer Science and Ubiquitous Computing. CUTE CSA 2017 2017. Lecture Notes in Electrical Engineering, vol 474. Springer, Singapore. https://doi.org/10.1007/978-981-10-7605-3_134

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-7605-3_134

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-7604-6

  • Online ISBN: 978-981-10-7605-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation