HOIDS-Based Detection Method of Vicious Event in Large Networks

  • Dong Hwi Lee
  • Jeom Goo Kim
  • Kuinam J. Kim
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4097)


It is very crucial in the field of security control to acquire the capability of promptly coping with various threatening elements in cyber world such as vicious worms, virus and hackings that cause enormous damage and loss across the nation within a rather short term period like the large scale network paralyzed by vicious traffic, disturbance of electronic commerce, etc. As such, it can be the fundamental measure on these sorts of threats to establish the new method of detecting the similar threats as well as to reinforce the user’s recognition of security. The purpose of this study is to analyze the problems in the existing IDS and TMS, which are monolithic in terms of detection method, and further to suggest the improved detection method and HOIDS system which is recently introduced and in test operation.


Intrusion Detection System Electronic Commerce Large Scale Network Security Control Short Term Period 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Vanderavero, N., Brouckaert, X., Bonaventure, O., Le Charlier, B.: The HoneyTank: a Scalable Approach to Collect Malicious Internet traffic. IEEE 2004, RTSS 2004, Session 1 (2004)Google Scholar
  2. 2.
    Seo, D.-i., Choi, Y.-s., Lee, S.-H.: Design and Development of Real Time Honeypot System for Collecting the Information of Hacker Activity. In: KIPS 2004, vol. 10(1), pp. 1941–1944 (2003)Google Scholar
  3. 3.
    Honeynet Project Overview (2005),
  4. 4.
    Hellerstein, J.L., Zhang, F., Shahabuddin, P.: A Statistical Approach to Predictive Detection. Computer Networks 35, 77–95 (2001)CrossRefGoogle Scholar
  5. 5.
    Zang, F., Hellerstein, J.L.: An Approach to On-line Predictive Detection. In: Preoceedings Of 8th International Symposium on Modeling. ASCTS (2000)Google Scholar
  6. 6.
    Groschwitz, N.K., Polyzos, G.C.: A Time Series Model of Long-Term NAFNET Backbone Traffic. In: proceedings of IEEE International Conference on Communications (1994)Google Scholar
  7. 7.
    Shu, Y., Yu, M., Liu, J.: Wireless traffic modeling and prediction using seasonal ARIMA models. In: Proceedings of IEEE International Conference on Communications, vol. 3 (2003)Google Scholar
  8. 8.
  9. 9.
    Moon, H.-K., Choe, J.-g., Kang, Y., Rhee, M.-s.: The study of correlation of threat and weakness in the network make it possible to forecast the cyber threat through the analysis of the correlation of N-IDS and VAS. Korea Institute of Information Security & Cryptology (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Dong Hwi Lee
    • 1
  • Jeom Goo Kim
    • 2
  • Kuinam J. Kim
    • 1
  1. 1.Dept. of Information Security Kyonggi Univ.Korea
  2. 2.Dept. of Computer Science Namseoul Univ.Korea

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