Authentication anomaly detection: A case study on a virtual private network

MineNet'07: Proceedings of the Third Annual ACM Workshop on Mining Network Data

Volumn , Issue , 2007, Pages 17-22

  Chapple, Michael J ^a   Chawla, Nitesh ^a   Striegel, Aaron ^a  

^a University of Notre Dame   (United States)

Author keywords

Anomaly detection; Authentication; Data mining; Security; Virtual private network

Indexed keywords

DATA MINING; MATHEMATICAL MODELS; NETWORK SECURITY; PERSONAL COMMUNICATION SYSTEMS; VIRTUAL REALITY;

ANOMALY DETECTION; VIRTUAL PRIVATE NETWORKS;

AUTHENTICATION;

EID: 34548268064     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1269880.1269886     Document Type: Conference Paper

References (16)

1. J. P. Anderson. Computer security threat monitoring and surveillance. Technical report, National Institute of Standards and Technology, 1980.
2. R. C. Brackney and R. H. Anderson. Understanding the insider threat. In Proceedings of a March 2004 Workshop, pages 1-137, Santa Monica, CA, USA, 2004. RAND National Security Research Division.
3. J. D. Brutlag. Aberrant behavior detection in time series for network monitoring. In LISA '00: Proceedings of the 14th USENIX Conference on System Administration, pages 139-146, Berkeley, CA, USA, 2000. USENIX Association.
4. J. B. D. Cabrera, B. Ravichandran, and R. K. Mehra. Statistical traffic modeling for network intrusion detection. In Proceedings of the 8th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, pages 466-473, Washington, DC, USA, 2000. IEEE.
5. A. C Deltac. Geo::distance perl module, 2005. Retreived on October 8, 2006 from http://search.cpan.org/bluefeet/Geo-Distance-0.11/Distance.pm.
6. M.-S. Kim, H.-J. Kang, S.-C. Hong, S.-H. Chung, and J. W. Hong. A flow-based method for abnormal network traffic detection. In Proceedings of the IEEE/IFIP Network Operations and Management Symposium, pages 599-612. IEEE, 2004.
7. K. Leung and C Leckie. Unsupervised anomaly detection in network intrusion detection using clusters. In Proc. 28th Australasian CS Conf., volume 38 of CRPITV, 2005.
8. MaxMind. Geolite city, 2006. Retrieved on October 8, 2006 from http://www.maxmind.com/app/geolitecity.
9. J. McHugh. Testing intrusion detection systems: A critique of the 1998 and 1999 DARPA intrusion detection system evaluations as performed by Lincoln Laboratory. A CM Trans. Inf. Syst. Secur., 3(4):262-294, 2000.
10. P. Meli, V. Hu, R. Lippmann, J. Haines, and M. Zissman. An overview of issues in testing intrusion detection systems. 2003. NIST IR 7046.
11. T.-L. Pai and P.-W. Wang. Netflow based intrusion detection system. In Proceedings of the 2004 IEEE International Conference on Networking, Sensing and Control, pages 731-736. IEEE, 2004.
12. L. Portnoy, E. Eskin, and S. Stolfo. Intrusion detection with unlabeled data using clustering. In ACM Workshop on Data Mining Applied to Security, 2001.
13. J. J. Treinen and R. Thurimella. A framework for the application of association rule mining in large intrusion detection infrastructures. In RAID 2006: Proceedings of the 9th Annual Symposium on Recent Advances in Intrusion Detection, pages 1-18. Springer Berlin / Heidelberg, 2006.
14. G. Tsudik and R. Summers. AudES: An expert system for security auditing. In Proceedings of the AAAI Conference on Innovative Application in Artificial Intelligence, 1990.
15. N. Z. University of Waikato. Waikato environment for data analysis v3.5.3, 2005. Retrieved on October 8, 2006 from http://www.cs.waikato.ac.nz/ml/ weka/.
16. I. H. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann Series in Data Management Systems. Morgan Kaufmann, second edition, June 2005.