Using immunological principles in anomaly detection

Intelligent Engineering Systems Through Artificial Neural Networks

Volumn 6, Issue , 1996, Pages 443-448

  Dasgupta, Dipankar ^a  

^a UNIVERSITY OF MISSOURI ST LOUIS   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; FAILURE ANALYSIS; IMMUNOLOGY; MONITORING; PERFORMANCE;

ANOMALY DETECTION;

ACCIDENT PREVENTION;

EID: 0030308775     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Article

References (9)

1. Caudell, T. P. and Newinan, D. S. (1993). An Adaptive Resonance Architecture to Define Normality and Detect Novelties in Time Series and Databases. In IEEE World Congress on Neural Networks, pages IV166-176, Portland, Oregon.
2. Dasgupta, D. and Forrest, S. (1995). Tool breakage detection in milling operations using a negative-selection algorithm. Technical Report Technical Report No. CS95-5, Department of Computer Science, University of New Mexico.
3. Dasgupta, D. and Forrest, S. (1996). Novelty detection in time series data using ideas from immunology. In ISCA 5th International Conference on Intelligent Systems, Reno, Nevada.
4. D'haeseleer, P., Forrest, S., and Helman, P. (1996). An immunological approach to change detection: algorithms, analysis, and implications. In Proceedings of IEEE Symposium on Research in Security and Privacy, Oakland, CA.
5. Forrest, S., Perelson, A. S., Allen, L., and Cherukuri, R. (1994). Self-Nonself Discrimination in a Computer. In Proceedings of IEEE Symposium on Research in Security and Privacy, pages 202-212, Oakland, CA.
6. Hwarng, H. B. and Chong, C. W. (1994). A Fast-Learning identification system for SPC: An Adaptive Resonance Theory approach. Intelligent Engineering Systems Through Artificial Neural Networks, 4:1097-1102.
7. Kozma, R., Kitamura, M., Sakuma, M., and Yokoyama, Y. (1994). Anomaly Detection by neural network models and statistical time series analysis. In Proceedings of IEEE International Conference on Neural Networks, Orlando, Florida.
8. Mackey, M. C. and Glass, L. (1977). Oscillation and Chaos in Physiological Control Sysyems. Science, 197:287-289.
9. Roberts, S. and Tarassenko, L. (1994). A Probabilistic Resource Allocating Network for Novelty Detection. Neural Computation, 6:270-284.