Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Self-Nonself Discrimination in a Computer
SP '94 Proceedings of the 1994 IEEE Symposium on Security and Privacy
A study of artificial immune systems applied to anomaly detection
A study of artificial immune systems applied to anomaly detection
Modelling danger and anergy in artificial immune systems
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Application of Fuzzy Logic to Approximate Reasoning Using Linguistic Synthesis
IEEE Transactions on Computers
Design of an Artificial Immune System for fault detection: A Negative Selection Approach
Expert Systems with Applications: An International Journal
Multivariable Gaussian Evolving Fuzzy Modeling System
IEEE Transactions on Fuzzy Systems
A transitional view of immune inspired techniques for anomaly detection
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
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This paper describes an immune-inspired system based on an alternate theory about the self-nonself distinction theory, which defines the negative selection process as a mechanism of a fuzzy system based on the affinity between antigen and T-cells. This theory may provide a decision making tool which improves the generation of detectors or even define new data monitoring in order to detect an extreme variation of the system behavior, which means anomalies occurrences. Through these algorithms, tests are performed to detect faults of a DC motor. Upon detection of faults, a participatory clustering algorithm is used to classify these faults and tested to obtain the best set of parameters to achieve the most accurate clustering for these tests in the application being discussed in the article.