Anomaly detection and diagnosis algorithms for discrete symbol sequences with applications to airline safety

IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews

Volumn 39, Issue 1, 2009, Pages 101-113

  Budalakoti, Suratna ^a,b   Srivastava, Ashok N ^c   Otey, Matthew E ^d,e  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF TEXAS AT AUSTIN   (United States)

^c NASA AMES RESEARCH CENTER   (United States)

^d MISSION CRITICAL TECHNOLOGIES INC   (United States)

^e GOOGLE INC   (United States)

Author keywords

Anomaly detection; Diagnosis; Discrete symbols; Fault detection; Integrated system health management (ISHM); Sequences

Indexed keywords

AIRCRAFT; ALGORITHMS; COCKPITS (AIRCRAFT); HEALTH; HIDDEN MARKOV MODELS; INTEGRATED CONTROL; INTEGRATED OPTICS; LEARNING ALGORITHMS; MARKOV PROCESSES; OBJECT RECOGNITION;

ANOMALY DETECTION; DIAGNOSIS; DISCRETE SYMBOLS; INTEGRATED SYSTEM HEALTH MANAGEMENT (ISHM); SEQUENCES;

FAULT DETECTION;

EID: 58649111729     PISSN: 10946977     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSMCC.2008.2007248     Document Type: Article

References (32)

1. A. N. Srivastava, "Discovering system health anomalies using data mining techniques," presented at the 2005 Joint Army Navy NASA Airforce Conf. Propulsion. Moffett Field, CA.
2. K. Sequeira and M. Zaki, "ADMIT: Anomaly based data mining for intrusions," in Proc. 8th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2002, pp. 386-395.
3. Y. Xiao and M. Dunham, "Interactive clustering for transaction data," in Proc. 3rd Int. Conf. Data Warehousing Knowl. Discovery, 2001, pp. 121-130.
4. F. Figueroa, R. Holland, J. Schmalzel, and D. Duncavage, "Integrated system health management (ISHM): Systematic capability implementation," in Proc. 2006 IEEE Sens. Appl. Symp., pp. 202-206.
5. A. M. Manning, A. Brass, C.A. Goble, and J. A. Keane, "Clustering techniques in biological sequence analysis," in Proc. 1st Eur. Symp. Principles Data Mining Knowl. Discovery, 1997, pp. 315-322.
6. J. Klema, L. Novakova, F. Karel, O. Stepankova, and F. Zelezny, "Sequential data mining: A comparative case study in development of atherosclerosis risk factors," IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 38, no. 1, pp. 3-15, Jan. 2008.
7. S. Budalakoti, A. Srivastava, and R. Akella, "Discovering atypical flights in sequences of discrete flight parameters," in Proc. 2006 IEEE Aerosp. Conf., pp. 1-8.
8. B. Hay, K. Vanhoof, and G. Wetsr, "Clustering navigation patterns on a website using a sequence alignment method," in Proc. 17th Int. Joint Conf. Artif. Intell., Seattle, WA, 2001, pp. 1-6.
9. C. Daw, C. Finney, and E. Tracy, "A review of symbolic analysis of experimental data," Rev. Sci. Instrum., vol. 74, no. 2, pp. 915-930, 2003.
10. J. Ghosh, "Scalable clustering methods for data mining," in Handbook of Data Mining. Mahwah, NJ: Lawrence Erlbaum, 2003, pp. 247-277.
11. L. R. Rabiner, "A tutorial on hidden Markov models and selected applications in speech recognition," Proc. IEEE, vol. 77, no. 2, pp. 257-286, Feb. 1989.
12. L. R. Rabiner, B. H. Juang, S. E. Levinson, and M. M. Sondhi, "Some properties of continuous hidden Markov model representations," AT&T Tech. J., vol. 64, no. 6, pp. 257-286, Jul./Aug. 1985.
13. C. Warrender, S. Forrest, and B. Pearlmutter, "Detecting intrusions using system calls: Alternative data models," in Proc. IEEE Symp. Security Privacy, 1999, pp. 133-145.
14. A. Banerjee and J. Ghosh, "Clickstream clustering using weighted longest common Subsequences," in Proc. Workshop Web Mining, SIAM Conf. Data Mining, Apr. 2001, pp. 33-40.
15. V. Levenshtein, "Binary codes capable of correcting deletions, insertions and reversals," Sov. Phys. - Dokl., vol. 10, no. 8, pp. 707-710, 1966.
16. T. Cormen, C. Leiserson, R. Rivest, and C. Stein, Introduction to Algorithms, 2nd ed. Cambridge, MA: MIT Press, 2001.
17. H. Kawaji, Y. Yamaguchi, H. Matsuda, and A. Hashimoto, "A graph-based clustering method for a large set of sequences using a graph partitioning algorithm," Genome Inf., vol. 12, pp. 93-102, 2001.
18. V. Guralnik andG.Karypis, "A scalable algorithm for clustering sequential data," in Proc. 1st IEEE Conf. Data Mining, 2001, pp. 179-186.
19. B. Szymanski and Y. Zhang, "Recursive data mining for masquerade detection and author identification," in Proc. 5th Annu. IEEE Int. Conf. Syst., Man, Cybern.. Inf. Assurance Workshop, 2004, pp. 424-431.
20. P. F. Evangelista, P. Bonnisone, M. J. Embrechts, and B. K. Szymanski, "Fuzzy ROC curves for the 1 class SVM: Application to intrusion detection," in Proc. 13th Eur. Symp. Artif. Neural Netw., 2005, pp. 345-350.
21. L. Kaufman and P. J. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis. New York: Wiley, 1990.
22. R. Ng and J.Han, "Clarans: A method for clustering objects for spatial data mining," IEEE Trans. Knowl. Data Eng., vol. 14, no. 5, pp. 1003-1016, Sep./Oct. 2002.
23. M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, "A density-based algorithm for discovering clusters in large spatial databases," in Proc. 2nd Int. Conf. Knowl. Discovery Data Mining, 1996, pp. 226-231.
24. M. Breunig, H.-P. Kriegel, R. Ng, and J. Sander, "LOF: Identifying density-based local outliers," in Proc. ACM SIGMOD Int. Conf. Manage. Data, 2000, pp. 93-104.
25. D. Gusfield, Algorithms on Strings, Trees, and Sequences. Cambridge, U.K.: Cambridge Univ. Press, 1997.
26. F. Nicolas and E. Rivals, "Hardness results for the center and median string problems under the weighted and unweighted edit distances," J. Discr. Alg., vol. 3, no. 2-4, pp. 390-415, Jun. 2005.
27. C. de la Higuera and F. Casacuberta, "Topology of strings: Median string is NP-complete," Theor. Comput. Sci., vol. 230, no. 1/2, pp. 39-48, 2000.
28. J.W. Hunt and T. G. Szymanski, "A fast algorithm for computing longest common subsequences," Commun. ACM, vol. 20, no. 5, pp. 350-353, May 1977.
29. D. S. Hirschberg, "Algorithms for the longest common subsequence problem," J. ACM, vol. 24, no. 4, pp. 664-675, Oct. 1977.
30. D. S. Hirschberg, "A linear space algorithm for computing maximal common subsequences," Commun. ACM, vol. 18, no. 6, pp. 341-343, Jun. 1975.
31. L. Bergroth, H. Hakonen, and T. Raita, "A survey of longest common subsequence algorithms," in Proc. 7th Int. Symp. String Process. Inf. Retrieval (SPIRE), 2000, pp. 39-48.
32. K. Murphy HMM Toolbox for Matlab [Online]. Available: http://www.cs. ubc.ca/murphyk