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
An Immunological Approach to Change Detection: Algorithms, Analysis and Implications
SP '96 Proceedings of the 1996 IEEE Symposium on Security and Privacy
A Classification Framework for Anomaly Detection
The Journal of Machine Learning Research
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
The effect of binary matching rules in negative selection
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Phase transition and the computational complexity of generating r-contiguous detectors
ICARIS'07 Proceedings of the 6th international conference on Artificial immune systems
On permutation masks in hamming negative selection
ICARIS'06 Proceedings of the 5th international conference on Artificial Immune Systems
Structural properties of shape-spaces
ICARIS'06 Proceedings of the 5th international conference on Artificial Immune Systems
An Empirical Study of Self/Non-self Discrimination in Binary Data with a Kernel Estimator
ICARIS '08 Proceedings of the 7th international conference on Artificial Immune Systems
On AIRS and Clonal Selection for Machine Learning
ICARIS '09 Proceedings of the 8th International Conference on Artificial Immune Systems
A generative model for self/non-self discrimination in strings
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
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Affinity functions are the core components in negative selection to discriminate self from non-self. It has been shown that affinity functions such as the r-contiguous distance and the Hamming distance are limited applicable for discrimination problems such as anomaly detection. We propose to model self as a discrete probability distribution specified by finite mixtures of multivariate Bernoulli distributions. As by-product one also obtains information of non-self and hence is able to discriminate with probabilities self from non-self. We underpin our proposal with a comparative study between the two affinity functions and the probabilistic discrimination.