Abstract
Recently, the spread of the Internet makes familiar to the incident concerning the Internet, such as a DoS attack and a DDoS attack. Some methods which detect the abnormal traffics in the network using the information from headers and payloads of IP-packets transmitted in the networks are proposed. In this research, we propose a method of Pareto Learning SOM (Self Organizing Map) for IP packet flow analysis in which the occurrence rate is used for SOM computing. The flow of the packets can be visualized on the map and it can be used for detecting the abnormal flows of packets.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Ohkouchi, K., Rikitake, K., Nakao, K.: A Study on Network Incident Analysis Using Self Organizing Maps. In: Proceedings of the 2006 Symposium on Cryptography and Information Security (2006)
Kanenishi, K., Togawa, S., Matsuura, K., Mitsuhara, H., Yano, Y.: Aberrant Detection from Behavior of Campus Network Traffic. Journal of Academic Computing and Networking (13), 74–83 (2009)
Lagus, K., Kaski, S., Kohonen, T.: Mining Massive Document Collections by the WEB- SOM method. Information Sciences 163/1-3, 135–156 (2004)
Abe, T., Ikemura, T., et al.: A Novel Bioinfor- matics Strategy for Phylogenetic Study of Genomic sequence Fragments: Self Organizing Map (SOM) of Oligonucleotide Frequencies. In: Proceedings of 5th Workshop on Self Organizing Maps, pp. 669–676 (2005)
Dozono, H., Nakakuni, M.: Application of Supervised Pareto Learning Self Organizing Maps amd Its Incremental Learning. In: Advances in Self Organizing Maps. LNCS, vol. 5629, pp. 54–62. Springer, Heidelberg (2009)
Dozono, H., Nakakuni, M.: Analysis of Robustness of Pareto Learning SOM to Variances of Input Vectors. In: Chan, J.H. (ed.) ICONIP 2009, Part II. LNCS, vol. 5864, pp. 836–844. Springer, Heidelberg (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dozono, H., Nakakuni, M., Kabashima, T., Hara, S. (2010). Analysis of Packet Traffics and Detection of Abnormal Traffics Using Pareto Learning Self Organizing Maps. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Models and Applications. ICONIP 2010. Lecture Notes in Computer Science, vol 6444. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17534-3_40
Download citation
DOI: https://doi.org/10.1007/978-3-642-17534-3_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17533-6
Online ISBN: 978-3-642-17534-3
eBook Packages: Computer ScienceComputer Science (R0)