Traffic Flooding Attack Detection Using SNMP MIB Variables and Decision Tree Classifier
Keywords:
SNMP, MIB, Decision Tree, TCP flooding, UDP flooding.Abstract
In emerging technology of Internet, security issues are becoming more challenging. The Internet has become an important source for information, entertainment, and a major means of communication at home and at work. With connectivity to the Internet, however, comes certain security threat. Unauthorized access, modifiers, denial of service, or complete control of machines by malicious users are all examples of security threats encountered on the Internet. Therefore, there is need for an approach, which will efficiently detect the flooding attacks in the network. The proposed system deals with Simple network management protocol based detection system to detect TCP and UDP flooding attacks effectively.
References
- Braga, R., Mota, E., & Passito, A. (2010, October). Lightweight DDoS flooding attack detection using NOX/OpenFlow. In Local Computer Networks (LCN), 2010 IEEE 35th Conference on (pp. 408-415). IEEE.
- Afanasyev, A., Mahadevan, P., Moiseenko, I., Uzun, E., & Zhang, L. (2013, May). Interest flooding attack and countermeasures in Named Data Networking. In IFIP Networking Conference, 2013(pp. 1-9). IEEE.
- Xiao, B., Chen, W., He, Y., & Sha, E. M. (2005, July). An active detecting method against SYN flooding attack. In Parallel and distributed systems, 2005. proceedings. 11th international conference on (Vol. 1, pp. 709-715). IEEE.
- Yu, J., Lee, H., Kim, M. S., & Park, D. (2008). Traffic flooding attack detection with SNMP MIB using SVM. Computer Communications, 31(17), 4212-4219.
- Park, J. S., & Kim, M. S. (2008). Design and implementation of an SNMP-based traffic flooding attack detection system. Challenges for next generation network operations and service management, 380-389.
- Li, J., & Manikopoulos, C. (2003, June). Early statistical anomaly intrusion detection of DOS attacks using MIB traffic parameters. In Information Assurance Workshop, 2003. IEEE Systems, Man and Cybernetics Society (pp. 53-59). IEEE.
- Ramah, K. H., Ayari, H., & Kamoun, F. (2006). Traffic anomaly detection and characterization in the tunisian national university network. Lecture notes in computer science, 3976, 136.
- Jun, J. H., Oh, H., & Kim, S. H. (2011, December). DDoS flooding attack detection through a step-by-step investigation. In Networked Embedded Systems for Enterprise Applications (NESEA), 2011 IEEE 2nd International Conference on (pp. 1-5). IEEE.
- Ahmed, E., Mohay, G., Tickle, A., & Bhatia, S. (2010). Use of ip addresses for high rate flooding attack detection. Security and Privacy–Silver Linings in the Cloud, 124-135.
- Streilein, W. W., Fried, D. J., & Cunningham, R. K. (2003, September). Detecting flood-based denial-of-service attacks with snmp/rmon. In Workshop on Statistical and Machine Learning Techniques in Computer Intrusion Detection, Fairfax, Virginia, USA.
- Stein, G., Chen, B., Wu, A. S., & Hua, K. A. (2005, March). Decision tree classifier for network intrusion detection with GA-based feature selection. In Proceedings of the 43rd annual Southeast regional conference-Volume 2 (pp. 136-141). ACM.
- Kruegel, C., & Toth, T. (2003). Using decision trees to improve signature-based intrusion detection. In Recent Advances in Intrusion Detection (pp. 173-191). Springer Berlin/Heidelberg.
- Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA data mining software: an update. ACM SIGKDD explorations newsletter, 11(1), 10-18.
Downloads
Published
Issue
Section
License
Copyright (c) IJSRCSEIT

This work is licensed under a Creative Commons Attribution 4.0 International License.