Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
권호 표지
DOI QR코드

DOI QR Code

Robust Real-time Intrusion Detection System

  • Kim, Byung-Joo (Department of Information & Communication Engineering at Youngsan University) ;
  • Kim, Il-Kon (Department of Computer Science at Kyungpook National University)
  • Published : 2005.12.01
Download PDF
⟨ Previous Next ⟩

Abstract

Computer security has become a critical issue with the rapid development of business and other transaction systems over the Internet. The application of artificial intelligence, machine learning and data mining techniques to intrusion detection systems has been increasing recently. But most research is focused on improving the classification performance of a classifier. Selecting important features from input data leads to simplification of the problem, and faster and more accurate detection rates. Thus selecting important features is an important issue in intrusion detection. Another issue in intrusion detection is that most of the intrusion detection systems are performed by off-line and it is not a suitable method for a real-time intrusion detection system. In this paper, we develop the real-time intrusion detection system, which combines an on-line feature extraction method with the Least Squares Support Vector Machine classifier. Applying the proposed system to KDD CUP 99 data, experimental results show that it has a remarkable feature extraction and classification performance compared to existing off-line intrusion detection systems.

Keywords

References

  1. I.T. Jolliffe, 'Principal Component Analysis', New York Springer-Verlag, 1986
  2. S. Chandrasekaran, B.S. Manjunath, Y.F. Wang, J. Winkeler and H. Zhang, 'An eigenspace update algorithm for image analysis,' Graphical Models and Image Processing, 59(5), pp.321-332, Sep., 1997 https://doi.org/10.1006/gmip.1997.0425
  3. J. Winkeler, B.S. Manjunath and S. Chandrasekaran, 'Subset selection for active object recognition,' In CVPR, volume 2, pp.511-516, IEEE Computer Society Press, Jun., 1999
  4. P. Hall, D. Marshall , and R. Martin, 'Incremental eigenanalysis for classification,' In British Machine Vision Conference, volume 1, pp.286-295 , Sep., 1998
  5. M.E. Tipping and C.M. Bishop, 'Mixtures of probabilistic principal component analysers,' Neural Computation 11(2), pp.443-482, 1998 https://doi.org/10.1162/089976699300016728
  6. M.A. Kramer, 'Nonlinear principal component analysis using autoassociative neural networks,' AICHE Journal 37(2), pp.233-243, 1991 https://doi.org/10.1002/aic.690370209
  7. K.I. Diamantaras and S.Y. Kung, 'Principal Component Neural Networks: Theory and Applications', New York John Wiley & Sons, Inc., 1996
  8. B. Scholkopf, A. Smola and K.R. Muller, 'Nonlinear component analysis as a kernel eigenvalue problem,' Neural Computation 10(5), pp.1299-1319, 1998
  9. R. Rosipal and M. Girolami, 'An Expectation Maximization approach to nonlinear component analysis,' Submitted
  10. B. Scholkopf, S. Mika, C. Burges, P. Knirsch, K.R. Miller, G. Ratsch and A.J. Smola, 'Input Space versus Feature Space in Kernel-Based Methods,' IEEE Transactions on Neural Networks , vol. 10, pp.1000-1017, Sep., 1999 https://doi.org/10.1109/72.788641
  11. AJ. Smola, O.L.Mangasarian, and B. Scholkopf, 'Sparse kernel feature analysis,' Technical Report 99-03, University of Wisconsin, Data Mining Institute, Madison, 1999
  12. S.R. Gunn, 'Support vector machines for classification and regression,' Technical Report ISIS-198, Department of Electronics and Computer Science, University of Southampton, 1998
  13. S. Mika, 'Kernel algorithms for nonlinear signal processing in feature spaces,' Master's thesis, Technical University of Berlin, November. 1998
  14. H. Murakami and V. Kumar, 'Efficient Calculation of Primary Images from a Set of Images,' IEEE Trans. On Pattern Analysis and Machine Intelligence, 4 (5), pp.511-515, 1982 https://doi.org/10.1109/TPAMI.1982.4767295
  15. V. N. Vapnik, 'Statistical learning theory', John Wiley & Sons, New York, 1998
  16. K. Tsuda, 'Support vector classifier based on asymmetric kernel function,' Proc. ESANN, 1999
  17. J.A.K. Suykens and Vandewalle, 'Least squares support vector machine classifiers ,' Neural Processing Letters, vol.9, pp.293-300, 1999 https://doi.org/10.1023/A:1018628609742
  18. Accessible at http://www.esat.kuleuven.ac.be/sista/ Issvmlab/tutorial

Cited by

  1. Extended semi-supervised fuzzy learning method for nonlinear outliers via pattern discovery vol.29, 2015, https://doi.org/10.1016/j.asoc.2014.12.034
  2. Practical In-Depth Analysis of IDS Alerts for Tracing and Identifying Potential Attackers on Darknet vol.9, pp.2, 2017, https://doi.org/10.3390/su9020262
  3. Multivariate correlation analysis and geometric linear similarity for real-time intrusion detection systems vol.8, pp.7, 2015, https://doi.org/10.1002/sec.1074