| On the use of co-occurrence matrices for network anomaly detection |
| Full text |
Pdf
(457 KB)
|
| Source
|
International Conference On Communications And Mobile Computing
archive
Proceedings of the 2009 International Conference on Wireless Communications and Mobile Computing: Connecting the World Wirelessly
table of contents
Leipzig, Germany
SESSION: Security I (Computer and Network Security symposium)
table of contents
Pages 96-100
Year of Publication: 2009
ISBN:978-1-60558-569-7
|
|
Authors
|
|
| Sponsors |
|
| Publisher |
|
| Bibliometrics |
Downloads (6 Weeks): 3, Downloads (12 Months): 20, Citation Count: 0
|
|
|
ABSTRACT
In the last few years the number and impact of security attacks over the Internet have been continuously increasing. Since it is impossible to guarantee complete protection to a system by means of the "classical" prevention mechanisms, the use of Intrusion Detection Systems (IDSs) has emerged as a key element in network security. In this paper we address the problem considering some techniques for detecting network anomalies, based on the use of co-occurrence matrices, to model the "normal" behavior of the TCP connections. The performance analysis, shows a comparison among the different solutions, which demonstrates the effectiveness of the proposed methods.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
| |
1
|
M. Turk and A. Pentland, "Face recognition using eigenfaces," in Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, (CVPR), 1991.
|
| |
2
|
|
| |
3
|
A. Pentland, B. Moghaddam, and T. Starner, "View-based and modular eigenspaces for face recognition," in Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 1994.
|
| |
4
|
M. Oka, Y. Oyama, H. Abe, and K. Kato, "Anomaly detection using layered networks based on eigen co-occurrence matrix," in Proc. of the International Symposium on Recent Advances in Intrusion Detection (RAID), pp. 223--237, 2004.
|
| |
5
|
M. Oka, Y. Oyama, and K. Kato, "Eigen co-occurrence matrix method for masquerade detection," in Proc. of the 7th JSSST SIGSYS Workshop on Systems for Programming and Applications (SPA), 2004.
|
| |
6
|
R. Haralick, Dinstein, and K. Shanmugam, "Textural features for image classification," IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-3, pp. 610--621, 1973.
|
| |
7
|
R. Walker, P. Jackway, and D. Longstaff, "Recent developments in the use of the co-occurrence matrix for texture recognition," in Proc. of the 13th International Conference on Digital Signal Processing (ICDSP), 1997.
|
| |
8
|
D. Benedetto, E. Caglioti, and V. Loreto, "Language trees and zipping," Physical Review Letters, vol. 88, January 2002.
|
| |
9
|
A. Puglisi, "Data compression and learning in time sequences analysis," 2002.
|
| |
10
|
"MIT, Lincoln laboratory, DARPA evaluation intrusion detection." http://www.ll.mit.edu/IST/ideval/ (accessed on 2008/06/28).
|
| |
11
|
C. Callegari, S. Vaton, and M. Pagano, "A new statistical approach to network anomaly detection," in Proc. of the International Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS), 2008.
|
|