Skip to main content
SpringerLink
Log in

An Investigation of Scalable Anomaly Detection Techniques for a Large Network of Wi-Fi Hotspots

  • Conference paper
  • First Online:
Scalable Information Systems (INFOSCALE 2014)

Included in the following conference series:

  • 283 Accesses

Abstract

The paper seeks to investigate the use of scalable machine learning techniques to address anomaly detection problem in a large Wi-Fi network. This was in the efforts of achieving a highly scalable preemptive monitoring tool for wireless networks. The Neural Networks, Bayesian Networks and Artificial Immune Systems were used for this experiment. Using a set of data extracted from a live network of Wi-Fi hotspots managed by an ISP; we integrated algorithms into a data collection system to detect anomalous performance over several test case scenarios. The results are revealed and discussed in terms of both anomaly performance and statistical significance.

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 34.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 44.99
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. Vaughan-Nichols, S.J.: The challenge of wi-fi roaming. Computer 36(7), 17–19 (2003)

    Article  Google Scholar 

  2. Cannady, J.: Artificial neural networks for misuse detection. In: Presented at National Information Systems Security Conference (1998)

    Google Scholar 

  3. Cheng, E., Jin, H., Han, Z., Sun, J.: Network-based anomaly detection using an elman network. In: Lu, X., Zhao, W. (eds.) ICCNMC 2005. LNCS, vol. 3619, pp. 471–480. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  4. Zhang, J., Zulkernine, M.: Anomaly based network intrusion detection with unsupervised outlier detection. In: Presented at IEEE International Conference On Communications, ICC 2006 (2006)

    Google Scholar 

  5. Forrest, S., Hofmeyr, S.A., Somayaji, A., Longstaff, T.A.: A sense of self for unix processes. In: Proceedings IEEE Symposium On Presented at Security and Privacy (1996)

    Google Scholar 

  6. Dasgupta, D., González, F.: An immunity-based technique to characterize intrusions in computer networks. IEEE Trans. Evol. Comput. 6(3), 281–291 (2002)

    Article  Google Scholar 

  7. Luther, K., Bye, R., Alpcan, T., Muller, A., Albayrak, S.: A cooperative AIS framework for intrusion detection. In: Presented at IEEE International Conference On Communications. ICC 2007 (2007)

    Google Scholar 

  8. Cha, B., Lee, D.: Network-based anomaly intrusion detection improvement by bayesian network and indirect relation. In: Apolloni, B., Howlett, R.J., Jain, L. (eds.) KES 2007, Part II. LNCS (LNAI), vol. 4693, pp. 141–148. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  9. Machaka, P., Bagula, A., De Wet, N.: A highly scalable monitoring tool for wi-fi networks. In: Presented at 2012 IEEE 1st International Symposium On Wireless Systems (IDAACS-SWS) (2012)

    Google Scholar 

  10. Machaka, P., Bagula, A.: Preemptive performance monitoring of a large network of wi-fi hotspots: an artificial immune system. In: Masip-Bruin, X., Verchere, D., Tsaoussidis, V., Yannuzzi, M. (eds.) WWIC 2011. LNCS, vol. 6649, pp. 494–504. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  11. Machaka, P., Mabande, T., Bagula, A.: Monitoring of a large wi-fi hotspots network: performance investigation of soft computing techniques. In: Hart, E., Timmis, J., Mitchell, P., Nakamo, T., Dabiri, F. (eds.) BIONETICS 2011. LNICST, vol. 103, pp. 155–162. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  12. Dunne, R.A.: A Statistical Approach to Neural Networks for Pattern Recognition, vol. 702. Wiley, New York (2007)

    Book  MATH  Google Scholar 

  13. Kjræulff, U.B., Madsen, A.L.: Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis, vol. 22. Springer, New York (2012)

    Google Scholar 

  14. Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques. Elsevier Inc., Oxford (2005)

    Google Scholar 

  15. Watkins, A., Timmis, J., Boggess, L.: Artificial immune recognition system (AIRS): an immune-inspired supervised learning algorithm. Genet. Program. Evolvable Mach. 5(3), 291–317 (2004)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Council for Scientific and Industrial Research Modelling and Digital Science, Meiring Naude Rd, Pretoria, 0184, South Africa

    Pheeha Machaka

  2. University of the Western Cape, Robert Sobukwe Road, Bellville, 7535, South Africa

    Antoine Bagula

Authors
  1. Pheeha Machaka
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Antoine Bagula
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pheeha Machaka .

Editor information

Editors and Affiliations

  1. Chung-Ang University, Seoul, Korea, Republic of (South Korea)

    Jason J. Jung

  2. University of Craiova, Craiova, Romania

    Costin Badica

  3. Eötvös Loránd University, Budapest, Hungary

    Attila Kiss

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Cite this paper

Machaka, P., Bagula, A. (2015). An Investigation of Scalable Anomaly Detection Techniques for a Large Network of Wi-Fi Hotspots. In: Jung, J., Badica, C., Kiss, A. (eds) Scalable Information Systems. INFOSCALE 2014. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 139. Springer, Cham. https://doi.org/10.1007/978-3-319-16868-5_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-16868-5_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16867-8

  • Online ISBN: 978-3-319-16868-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation