Machine learning approaches to network anomaly detection

2nd Workshop on Tackling Computer Systems Problems with Machine Learning Techniques, SysML 2007, co-located with NSDI 2007

Volumn , Issue , 2007, Pages

  Ahmed, Tarem ^a   Oreshkin, Boris ^a   Coates, Mark ^a  

^a MCGILL UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ACCIDENTS; ANOMALY DETECTION; DATA TRANSFER; INTERNET PROTOCOLS; LEARNING ALGORITHMS; SECURITY SYSTEMS; SIGNAL DETECTION; TRAFFIC SURVEYS;

ANOMALY-DETECTION ALGORITHMS; DISTRIBUTED VIDEO SURVEILLANCES; LARGE DATA TRANSFERS; MACHINE LEARNING APPROACHES; MACHINE LEARNING TECHNIQUES; NETWORK ANOMALY DETECTION; ONLINE ANOMALY DETECTION; RECURSIVE KERNEL;

MACHINE LEARNING;

EID: 85092755815     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. J. Brutlag, “Aberrant behavior detection in time series for network monitoring,” in Proc. USENIX System Admin. Conf. (LISA), New Orleans, LA, Dec. 2000.
2. H. Hajji, “Statistical analysis of network traffic for adaptive faults detection,” IEEE Trans. Neural Networks, vol. 16, no. 5, pp. 1053-1063, Sep. 2005.
3. K. Yamanish, J.-I. Takeuchi, G. Williams, and P. Milne, “On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms,” Data Mining and Knowledge Discovery, vol. 8, no. 3, pp. 275-300, May 2004.
4. A. Lakhina, K. Papagiannaki, M. Crovella, C. Diot, E. Kolaczyk, and N. Taft, “Structural analysis of network traffic flows,” in Proc. ACM SIGMETRICS, New York, NY, Jun. 2004.
5. A. Lakhina, M. Crovella, and C. Diot, “Diagnosing network-wide traffic anomalies,” in Proc. ACM SIGCOMM, Portland, OR, Aug. 2004.
6. A. Lakhina, M. Crovella, and C. Diot, “Mining Anomalies Using Traffic Feature Distributions,” in Proc. ACM SIGCOMM, Philadelphia, PA, Aug. 2005.
7. L. Huang, X. Nguyen, M. Garofalakis, M. Jordan, A. Joseph, and N. Taft, “In-network PCA and anomaly detection,” in Advances in Neural Information Processing Systems, 19th ed., B. Schölkopf, J. Platt and T. Hoffman, Ed. Cambridge, MA: MIT Press, 2007, to appear.
8. A. Gardner, A. Krieger, G. Vachtsevanos, and B. Litt, “One-class novelty detection for seizure analysis from intracranial EEG,” J. Machine Learning Research (JMLR), vol. 7, pp. 1025-1044, Jun. 2006.
9. D. Barbará, C. Domeniconi and J. Rogers, “Detecting outliers using transduction and statistical testing,” in Proc. ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD), Philadelphia, PA, Aug. 2003.
10. J. Ma and S. Perkins, “Online novelty detection on temporal sequences,” in Proc. ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD), Washington, DC, Aug. 2003.
11. A. Ihler, J. Hutchins, and P. Smyth, “Adaptive event detection with time-varying Poisson processes,” in Proc. ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD), Philadelphia, PA, Aug. 2006.
12. H. Teng, K. Chen, and S. Lu, “Adaptive real-time anomaly detection using inductively generated sequential patterns,” in Proc. IEEE Comp. Soc. Symp. Research in Security and Privacy, Oakland, CA, May 1990.
13. T. Singliar and M. Hauskrecht, “Towards a learning traffic incident detection system,” in Proc. Workshop on Machine learning Algorithms for Surveillance and Event Detection, Pittsburgh, PA, Jun. 2006.
14. A. Muñoz and J. Moguerza, “Estimation of high-density regions using one-class neighbor machines,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 3, pp. 476-480, Mar. 2006.
15. T. Ahmed, M. Coates, and A. Lakhina, “Multivariate online anomaly detection using kernel recursive least squares,” in Proc. IEEE Infocom, Anchorage, AK, May 2007, to appear.
16. M. Davenport, R. Baraniuk, and C. Scott, “Learning minimum volume sets with support vector machines,” in Proc. IEEE Int. Workshop on Machine Learning for Signal Processing (MLSP), Maynooth, Ireland, Sep. 2006.
17. C. Scott and R. Nowak, “Learning minimum volume sets,” J. Machine Learning Research (JMLR), vol. 7, pp. 665-704, Apr. 2006.
18. Transports Quebec. Organization webpage. [Online]. Available: http://www.mtq.gouv.qc.ca/en/information/cameras/montreal/index.asp
19. Y. Engel, S. Mannor, and R. Meir, “The kernel recursive least squares algorithm,” IEEE Trans. Signal Proc., vol. 52, no. 8, pp. 2275-2285, Aug. 2004.