A new data mining based network intrusion detection model

2010 International Conference on Computer and Communication Technology, ICCCT-2010

Volumn , Issue , 2010, Pages 731-735

  Gudadhe, Mrudula ^a   Prasad, Prakash ^a   Wankhade, Kapil ^a  

^a PRIYADARSHINI COLLEGE OF ENGINEERING   (India)

Author keywords

Boosted decision trees; Data mining; Ensemble approach; Network intrusion detection system

Indexed keywords

ATTACK METHODS; BOOSTED DECISION TREES; COMPUTING ENVIRONMENTS; CURRENT SIGNATURES; DOMAIN KNOWLEDGE; ENSEMBLE APPROACH; EXISTING METHOD; EXPERT KNOWLEDGE; INTELLIGENT INTRUSION DETECTION SYSTEMS; INTRUSION DETECTION SYSTEMS; NETWORK INTRUSION DETECTION; NETWORK INTRUSION DETECTION SYSTEMS; SECURE NETWORKS;

COMPUTER CRIME; DATA MINING; DECISION TREES;

INTRUSION DETECTION;

EID: 78650565300     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCCT.2010.5640375     Document Type: Conference Paper

References (18)

1. C. C. Aggrawal, P. Yu, "Outlier Detection for High Dimensional Data", Proceedings of the ACM SIGMOD Conference, 2001.
2. P. P. Angelov, X. Zhou, "Evloving fuzzy rule based classifiers from data streams", IEEE Transaction on Fuzzy Systems, Vol 16, No. 6, pp. 1462- 1475, 2008.
3. R. Bane, N. Shivsharan, "Network intrusion detection system (NIDS)", pp. 1272-1277, 2008.
4. V. Barnett, T. Lewis, "Outliers in Statistical Data", John Wiley and Sons, NY, 1994.
5. S. T. Brugger, "Data mining methods for network intrusion detection", pp. 1-65, 2004.
6. M. M. Breunig, H. P. Kriegel, R. T. Ng, J. Sander, "LOF: Identifying Density-Based Local Outliers", Proceedings of the ACM SIGMOD Conference , 2000.
7. D. E. Denning, "An Intrusion Detection Model", IEEE Trans-actions on Software Engineering, SE-13, pp. 222-232, 1987.
8. H. S. Javitz, A. Valdes, "The NIDES Statistical Component: Description and Justification", Technical Report, Computer Science Laboratory, SRI International, 1993.
9. E Knorr, Ng, R.: Algorithms for Mining Distance-based Outliers in Large Data Sets. Proceedings of the VLDB Conference (1998)
10. W. Lee, S. J. Stolfo, "Data Mining Approaches for Intrusion Detection", Proceedings of the 1998 USENIX Security Symposium, 1998.
11. R. P. Lippmann, D. J. Fried, I. Graf, J. W. Haines, K. P. Kendall, D. McClung, D. Weber, S. E. Webster, D. Wyschogrod, R. K. Cunningham, M. A. Zissman, "Evaluating Intrusion Detection Systems: The 1998 DARPA Off-line Intrusion Detection Evaluation. Proceedings DARPA Information Survivability Conference and Exposition (DISCEX) 2000", Vol 2, pp. 12--26, IEEE Computer Society Press, Los Alamitos, CA, 2000.
12. W. Lee, S. J. Stolfo, "Data mining approaches for intrusion detection" Proc. of the 7th USENIX Security Symp.. San Antonio, TX, 1998.
13. W. Lee, S. J. Stolfo, K. W. Mok, "A data mining framework for building intrusion detection models", Proc. of the 1999 IEEE Symp.on Security and Privacy, pp. 120-132. Oakland, CA, 1999.
14. M. Masud, J. Gao, L. Khan, J. Han, "Classifying evolving data streams for intrusion detection".
15. M. Panda, M. Patra, "Ensemble rule based classifiers for detecting network intrusions", pp 19-22, 2009
16. S. Ramaswami, R. Rastogi, K.Shim, "Efficient Algorithms for Mining Outliers from Large Data Sets", Proceedings of the ACM SIGMOD Conference, 2000.
17. H. Wang, W. Fan, P. Yu, J. Han, "Mining concept-drifting data streams using ensemble classifiers", In Proceedings of the ACM SIGKDD, pp. 226-235, Washington DC, 2003.
18. Z. Yu, J. Chen, T. Q. Zhu, "A novel adaptive intrusion detection system based on data mining", pp.2390-2395, 2005.