Combating imbalance in network intrusion datasets

2006 IEEE International Conference on Granular Computing

Volumn , Issue , 2006, Pages 732-737

  Cieslak, David A ^a   Chawla, Nitesh V ^a   Striegel, Aaron ^a  

^a UNIVERSITY OF NOTRE DAME   (United States)

Author keywords

Classification; Computer network security; Imbalanced datasets; ROC curves

Indexed keywords

DATA MINING; DATA TRANSFER; DATABASE SYSTEMS; LEARNING SYSTEMS; SECURITY OF DATA;

COMPUTER NETWORK SECURITY; IMBALANCED DATASETS; ROC CURVES;

COMPUTER NETWORKS;

EID: 33751105239     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (23)

1. W. Leland, M. Taqqu, W. Willinger, and D. Wilson, "On the self-similar nature of Ethernet traffic (extended version)," IEEE/ACM Trans. Networking, vol. 2, pp. 1-15, 1994.
2. J. Vehel, E. Lutton, and C. Tricot, Fractals in Enginnering: From Theory to Industrial Applications. New York:Springer-Verlag, 1997.
3. R. Lippmann, D. Fried, I. Graf, J. Haines, K. Kendall, D. McClung, D. Weber, S. Webster, D. Wyschogrod, R. Cunningham, and M. Zissman, "The 1998 DARPA Off-line Intrusion Detection Evaluation," in Proc. of DARPA Information Survivability Conference and Exposition (DISCEX), Los Alamitos, CA, January 2000, pp. 12-26.
4. M. Roughan, T. Griffin, Z. Morley Mao, A. Greenberg, and B. Freeman, "IP Forwarding Anomalies and Improving their Detection Using Multiple Data Sources," in Proc. of ACM SIGCOMM 2004 Workshop on Network Troubleshooting: Research, Theory and Operations Practice Meet Malfunctioning Reality, August 2004.
5. Y. Zhang, S. Singh, S. Sen, N. Duffield, and C. Lund, "Online Identification of Hierarchical Heavy Hitters: Algorithms, Evalutaion, and Applications," in Proc. of ACM SIGCOMM Internet Measurement Conference, October 2004.
6. G. Weiss, "Mining with Rarity: A Unifying Framework," SIGKDD Explorations, vol. 6, no. 1, pp. 7-19, 2004.
7. S. Axelsson, "The Base-Rate Fallacy and the Difficulty of Intrusion Detection," Information and System Security, vol. 3, no. 3, pp. 186-205, 2000.
8. J. McHugh, "The 1998 Lincoln Laboratory IDS Evaluation (A Critique)," in Proc. of the Recent Advances in Intrusion Detection, October 1998, pp. 145-161.
9. W. Cohen, "Fast effective rule induction," in Proc. of Machine Learning: the 12th International Conference, July 1995, pp. 115-123.
10. N. Chawla, K. Bowyer, L. Hall, and W. Kegelmeyer, "SMOTE: Synthetic Minority of Over-sampling TEchique," Journal of Artificial Intelligence Resarch, vol. 16, pp. 341-378, 2002ta Mining for Network Intrusion. Detection: How to Get Started, MITRE Techni.
11. A. Lazarevic, L. Ertoz, V. Kumar, A. Ozgur, and J. Srivastava, "A Comparative Study of Anomaly Detection Schemes in Network Intrusion Detection," in Proc. of 3rd SIAM Conference on Data Mining, January 2003.
12. M. Thottan and C. Ji, "Anomaly Detection in IP Networks," IEEE Transactions on Signal Processing, vol. 51, pp. 2191-2204, 2003.
13. W. Lee and S. Stolfo, "Data Mining Approaches for Intrusion Detection," in Proc. of the 7th USENIX Security Symposium, January 1998.
14. E. Bloedom, A. Christiansen, W. Hill, C. Skorupka, L. Talbot, and J. Tivel, "Data Mining for Network Intrusion Detection: How to Get Started," Mitre, Tech. Rep., 2001.
15. H. Javitz and A. Valdes, "The NIDES Statistical Component: Description and Justification," Computer Science Lab, SRI Interational, Tech. Rep., 1994.
16. Sourcefire, Snort Users Manual: The Snort Project, 2005.
17. N. Japkowicz, "The Class Imbalance Problem: Significance and Strategies," in Proc. of the 2000 International Conference on Artificial Intelligence (IC-AI'2000): Special Track on Inductive Learning, vol. 1, 2000, pp. 111-117.
18. G. Weiss and F. Provost, "Learning when Training Data are Costly: The Effect of Class Distribution on Tree Induction," Journal of Artificial Intelligence Research, vol. 19, pp. 315-354, 2003.
19. N. V. Chawla, N. Japkowicz, and A. Kolcz, "Editorial: Special Issue on Learning from Imbalanced Data sets," SIGKDD Explorations, vol. 6, no. 1, pp. 1-6, 2004.
20. J. Laurikkala, "Improving Identification of Difficult Small Classes by Balancing Class Distribution," University of Tampere, Tech. Rep. A-2001-2, 2001.
21. S. Cost and S. Salzberg, "A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features," Machine Learning, vol. 10, no. 1, pp. 57-78, 1993.
22. J. Swets, "Measuring the Accuracy of Diagnostic Systems," Science, vol. 240, pp. 1285-1293, 1988.
23. Provost, F. and Fawcett, T., "Robust Classification for Imprecise Environments," Machine Learning, vol. 42, no. 3, pp. 203-231, 2001.