Home Gateway with Automated Real-Time Intrusion Detection for Secure Home Networks

  • Hayoung Oh
  • Jiyoung Lim
  • Kijoon Chae
  • Jungchan Nah
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3983)


Home networks will be widely established in residential areas. Intrusion detection is an important function in the home gateway because various networks try to access to home networks. We propose the home gateway with the automated real time intrusion detection adjustable in home network environment using the clustering methodology and the correlation. Our proposed model showed the reasonable misclassification rates.


Intrusion Detection Anomaly Detection Intrusion Detection System Home Network Supervise Learning Technique 
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.


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  1. 1.
    Saito, T., Tomoda, I., Takabatake, Y., Arni, J., Teramoto, K.: Home gateway architecture and its implementation. IEEE Transactions on Consumer Electronics, 1161–1166 (November 2000)Google Scholar
  2. 2.
    Jiang, Z., Kim, S., Lee, K., Bae, H., Kim, S.: Security Service Framework for Home Network. In: The Fourth Annual ACIS International Conference on Computer and Information Science (ICIS 2005), July 2005, pp. 233–238 (2005)Google Scholar
  3. 3.
    Jin, S.-Y., Yeung, D.S.: DDoS detection based on feature space modeling. In: 2004 International Conference on Machine Learning and Cybernetics, August 2004, pp. 4210–4215 (2004)Google Scholar
  4. 4.
    Germano, T.: Self Organizing Maps, Available in http://davis.wpi.edu/~matt/courses/soms/
  5. 5.
    Gonzalez, F., Dasgupta, D.: Neuro-Immune and Self-Organizing Map Approaches to Anomaly Detection: A comparison. In: ICARIS (2002)Google Scholar
  6. 6.
    Jirapummin, C., Wattanapongsakorn, N., Kanthamanon, P.: Hybrid Neural Networks for Intrusion Detection System. King Mongkut’s University of Technology Thonburi (2001)Google Scholar
  7. 7.
    Pearson Correlation Coefficient, Available in http://www.indstate.edu/nurs/mary/N322/pearsonr.html/
  8. 8.
  9. 9.
    Gunes Kayacik, H., Nur Zuncir-Heywood, A., Heywood, M.I.: On the Capability of an SOM based Intrusion Detection System. In: International Joint Conference on Neural Networks (2003)Google Scholar
  10. 10.
    Vesanto, J., Himberg, J., Alhoniemi, E., Parhankangas, J.: SOM Toolbox for Matlab 5, SOM Toolbox Team, Helsinki University of Technology (2000)Google Scholar
  11. 11.
    Kim, M., Na, H., Chae, K., Bang, H., Na, J.: A Combined Data Mining Approach for DDos Attack Detection. In: Kahng, H.-K., Goto, S. (eds.) ICOIN 2004. LNCS, vol. 3090, pp. 943–950. Springer, Heidelberg (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hayoung Oh
    • 1
  • Jiyoung Lim
    • 2
  • Kijoon Chae
    • 1
  • Jungchan Nah
    • 3
  1. 1.Dept. of Computer EngineeringEwha Womans UniversitySeoulKorea
  2. 2.Dept. of Internet and InformationKorean Bible UniversitySeoulKorea
  3. 3.Protocol Engineering CenterETRIDaejeonKorea

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