Abstract
Network security is an important issue in maintaining the Internet as an important social infrastructure. Especially, finding excessive consumption of network bandwidth caused by P2P mass flow, finding internet viruses, and finding DDoS attacks are important security issues. Although stream mining techniques seem to be promising techniques for network security, extensive network flow prevents the simple application of such techniques. Since conventional methods require non-realistic memory resources, a mining technique which works well using limited memory is required. This paper proposes a sampling-based mining method to achieve network security. By analyzing the characteristics of the proposed method with real Internet backbone flow data, we show the advantages of the proposed method, i.e. less memory consumption.
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.
References
Rfc2330, framework for ip performance metrics
Mirkovic, J., Prier, G., Reiher, P.L.: Attacking ddos at the source. In: Proc. of the 10th IEEE International Conference on Network Protocols, pp. 312–321. IEEE Computer Society Press, Los Alamitos (2002)
Ohsita, Y., Ata, S., Murata, M., Murase, T.: Detecting distributed denial-of-service attacks by analyzing tcp syn packets statistically. In: Proc. of IEEE Globecom 2004, IEEE Computer Society Press, Los Alamitos (2004)
Demaine, E.D., Lopez-Ortiz, A., Munro, J.I.: Frequency estimation of internet packet streams with limited space. In: Möhring, R.H., Raman, R. (eds.) ESA 2002. LNCS, vol. 2461, Springer, Heidelberg (2002)
Fang, M., Shivakumar, N., Garcia-Molina, H., Motwani, R., Ullman, J.D.: Computing iceberg queries efficiently. In: Proc. 24th Int. Conf. Very Large Data Bases, VLDB, pp. 299–310 (1998)
Manku, G., Motwani, R.: Approximate frequency counts over data streams. In: Proc. of the 28th International Conference on Very Large Data Bases, Hong Kong, China, pp. 346–357 (2002)
Charikar, M., Chen, K., Farach-Colton, M.: Finding frequent items in data streams (2002)
Jin, C., Qian, W., Sha, C., Yu, J.X., Zhou, A.: Dynamically maintaining frequent items over a data stream. In: Proc. of the twelfth international conference on Information and knowledge management, pp. 287–294 (2003)
Cormode, G., Muthukrishnan, S.: What’s hot and what’s not: Tracking frequent items dynamically. In: Proc. of Principles of Database Systems, pp. 296–306 (2003)
http://sunsite.informatik.rwth-aachen.de/publications/ceur-ws//vol-126/
Golab, L., DeHaan, D., Demaine, E., Lopez-Ortiz, A., Munro, J.I.: Identifying frequent items in sliding windows over on-line packet streams. In: Proc. of ACM SIGCOMM Internet Measurement Conference, Miami, USA, ACM Press, New York (2003)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Yoshida, K. (2007). Sampling-Based Stream Mining for Network Risk Management. In: Washio, T., Satoh, K., Takeda, H., Inokuchi, A. (eds) New Frontiers in Artificial Intelligence. JSAI 2006. Lecture Notes in Computer Science(), vol 4384. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69902-6_32
Download citation
DOI: https://doi.org/10.1007/978-3-540-69902-6_32
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69901-9
Online ISBN: 978-3-540-69902-6
eBook Packages: Computer ScienceComputer Science (R0)