Skip to main content

Command and Block Profiles for Legitimate Users of a Computer Network

  • Conference paper
Computer Information Systems – Analysis and Technologies

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 245))

Abstract

Intruders and masqueraders are a plague in computer networks. To recognize an intruder, one firstly needs to know what is the normal behavior of a legitimate user. To find it out, we propose to build pairs of profiles called ‘command and block profiles’. Schonlau data (SEA) are used for illustration of the concept and its usability in work with real data. The elaborated data contain observations for 50 users; for each of them a sequence of 15,000 system calls was recorded. Data for 21 users are pure; data for the remaining 29 users are contaminated with activities of alien (illegitimate) users. We consider only the uncontaminated data (for the 21 users). 5 out of 21 investigated users seem to change their profiles during work time. Some trials have shown that the proposed simple method may also recognize a big part of alien implanted blocks.

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

Access this chapter

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 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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Schonlau, M.: Masquerading used data, web page, http://www.schonlau.net

  2. Schonlau, M., et al.: Computer intrusion: detecting masquerades. Statistical Science 16, 1–17 (2001)

    MathSciNet  MATH  Google Scholar 

  3. Bartkowiak, A.M.: Anomaly, novelty, one-class classification: a comprehensive introduction. International Journal of Computer Systems and Industrial Management Applications 3, 061–071 (2011), http://www.mirlabs.net/ijcisim/index.html

    Google Scholar 

  4. Kim, H.-S., Cha, S.-S.: Empirical evaluation of SVM-based masquerade detection using UNIX command. Computers & Security 24, 160–168 (2005)

    Article  Google Scholar 

  5. Guan, X., Wang, W., Zhang, X.: Fast intrusion detection based on non-negative matrix factorization model. J. of Network and Computer Applications 32, 31–44 (2009)

    Article  Google Scholar 

  6. Wang, W., Guan, X., Zhang, X.: Processing of massive audit data streams for real-time anomaly intrusion detection. Computer Communications 31, 58–72 (2008)

    Article  Google Scholar 

  7. DiGesu, V., LoBosco, G., Friedman, J.H.: Intruders pattern identification, pp. 1–4. IEEE (2008) 978-1-4244-2175-6/08 ©2008

    Google Scholar 

  8. Sodiya, A.S., Folorunso, O., Onashoga, S.A., Ogunderu, O.P.: An improved semi-global alignement algorithm for masquerade detection. Int. J. for Network Security 13, 31–40 (2011)

    Google Scholar 

  9. Bertacchini, M., Fierens, P.I.: Preliminary results on masquerader detection using compression based similarity metrics. Electronic Journal of SADIO 7(1), 31–42 (2007), http://www.dc.uba.ar/sadio/ejs

    MATH  Google Scholar 

  10. Posadas, R., Mex-Perera, C., Monroy, R., Nolazco-Flores, J.: Hybrid Method for Detecting Masqueraders Using Session Folding and Hidden Markov Models. In: Gelbukh, A., Reyes-Garcia, C.A. (eds.) MICAI 2006. LNCS (LNAI), vol. 4293, pp. 622–631. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  11. Salem, M.B., Hershkop, S., Stolfo, S.J.: A survey of insider attack detection research. In: Insider Attack and Cyber Security: Beyond the Hacker, pp. 69–90. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  12. Bartkowiak, A.: Outliers in biometrical data: what’s old, what’s new. Int. J. of Biometrics 2(1), 2–18 (2010)

    Article  Google Scholar 

  13. Kohonen, T.: Self-organising maps. Springer, Heidelberg (1995)

    Book  Google Scholar 

  14. Vesanto, J., Himberg, J., Alhoniemi, E., Parhankangas, J.: SOM Toolbox for Matlab 5. Som Toolbox team, Helsinki University of Technology, Finland, Libella Oy, Espoo, 1–54 (2000), http://www.cis.hut.fi/projects/somtoolbox/

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Bartkowiak, A.M. (2011). Command and Block Profiles for Legitimate Users of a Computer Network. In: Chaki, N., Cortesi, A. (eds) Computer Information Systems – Analysis and Technologies. Communications in Computer and Information Science, vol 245. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27245-5_35

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-27245-5_35

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27244-8

  • Online ISBN: 978-3-642-27245-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics