Principal component-based anomaly detection scheme

Studies in Computational Intelligence

Volumn 9, Issue , 2006, Pages 311-329

  Shyu, Mei Ling ^a   Chen, Shu Ching ^b   Sarinnapakorn, Kanoksri ^a   Chang, Liwu ^c  

^a University of Miami College of Engineering   (United States)

^b Florida International University   (United States)

^c NAVAL RESEARCH LABORATORY   (United States)

Author keywords

Anomaly detection; data mining; intrusion detection; outliers; principal component analysis; principal component classifier

Indexed keywords

EID: 78049278861     PISSN: 1860949X     EISSN: None     Source Type: Book Series    
DOI: 10.1007/11539827-18     Document Type: Conference Paper

References (25)

1. Aggarwal CC, Yu PS (2001) Outlier detection for high dimensional data. In: The ACM SIGMOD Conference, Santa Barbara, California, pp 37-46
2. th World Multiconference on Systemics, Cybernetics and Informatics, Orlando, Florida.
3. Breunig MM, Kriegel H-P, Ng RT, Sander J (2000) LOF: Identifying densitybased local outliers. In: Proc. of the ACM SIGMOD Conference, Dallas, Texas, pp 93-104
4. Denning DE (1987) An intrusion detection model. IEEE Transactions on Software Engineering, SE-13, pp 222-232
5. Dokas P, Ertoz L, Kumar V, Lazarevic A, Srivastava J, Tan P-N (2002) Data mining for network intrusion detection. In: Proc. of the National Science Foundation Workshop on Next Generation Data Mining. pp 21-30
6. Emran SM, Ye N (2001) Robustness of Canberra metric in computer intrusion detection. In: Proc. of the IEEE Workshop on Information Assurance and Security, United States Military Academy, West Point, New York, pp 80-84
7. Gnanadesikan R, Kettenring JR (1972) Robust estimates, residuals and outlier detection with multiresponse data. Biometrics, 28:81-124
8. Hawkins DM (1974) The detection of errors in multivariate data using principal components. Journal of the American Statistical Association, vol 69, no 346:340-344
9. Heady R, Luger G, Maccabe A, Servilla M (1990) The architecture of a network level intrusion detection system, Technical Report, Computer Science Department, University of New Mexico
10. Hu W, Liao Y, Vemuri VR (2003) Robust support vector machines for anomaly detection in computer security. In: Proc. of the International Conference on Machine Learning and Applications (ICMLA), Los Angeles, California, pp 168-174
11. Javitz HS, Valdes A (1991) The SRI statistical anomaly detector. In: Proc. of the IEEE Symposium on Research in Security and Privacy, Oakland, California, pp 316-326
12. Jobson JD (1992) Applied multivariate data analysis, Volume II: Categorical and multivariate methods. Springer-Verlag, New York
13. th edn. Prentice-Hall, New Jersey
14. nd edn. Springer-Verlag, New York
15. th edn. Oxford University Press, New York
16. KDD Cup 1999 Data, Available on: http://kdd.ics.uci.edu/databases/ kddcup99/kddcup99.html
17. rd SIAM Conference on Data Mining
18. Lee W, Stolfo SJ (2000) A framework for constructing features and models for intrusion detection systems. ACM Transactions on Information and System Security, 3:227-261
19. Lippmann RP, Fried DJ, Graf I, Haines JW, Kendall KP, McClung D, Weber D, Webster SE, Wyschogrod D, Cunningham RK, Zissman MA (2000) Evaluating intrusion detection systems: The 1998 DARPA off-line intrusion detection evaluation. In: Proc. of the DARPA Information Survivability Conference and Exposition (DISCEX) 2000, vol 2, IEEE Computer Society Press, California, pp 12-26
20. Markou M, Singh S (2003) Novelty detection: A review, Part 1: Statistical approaches. Signal Processing, 83, 12:2481-2497
21. Markou M, Singh S (2003) Novelty detection: A review, Part 2: Neural network-based approaches. Signal Processing, 83, 12:2499-2521
22. st International Conference on Dependable Systems & Networks, pp 623-630
23. Shyu M-L, Chen S-C, Sarinnapakorn K, Chang L (2003) A novel anomaly detection scheme based on principal component classifier. In: Proc. of the IEEE Foundations and New Directions of Data Mining Workshop, in conjunction with the Third IEEE International Conference on Data Mining (ICDM'03), Melbourne, Florida, pp 172-179
24. Ye N, Chen Q (2001) An anomaly detection technique based on a chi-square statistic for detecting intrusions into information systems. Quality and Reliability Eng. Int'l, 17:105-112
25. Ye N, Emran SM, Chen Q, Vilbert S (2002) Multivariate statistical analysis of audit trails for host-based intrusion detection. IEEE Transactions on Computers, 51:810-820