Abstract
In abnormal detection, the frequency of abnormal activities is changed over the time, so it is reasonable that detection algorithms can be adapted to the frequency’s change. In this work, an adaptive mathematic model is proposed to improve the adaptive capability. Then an augmented hybrid immune detector maturation algorithm applied in anomaly detection is presented. Experiment results show the algorithm can be adapted to the frequency’s change.
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Chen, J., Zhang, S., Chen, D. (2014). Improving the Frequency Adaptive Capability of Hybrid Immune Detector Maturation Algorithm. In: Sun, Xh., et al. Algorithms and Architectures for Parallel Processing. ICA3PP 2014. Lecture Notes in Computer Science, vol 8630. Springer, Cham. https://doi.org/10.1007/978-3-319-11197-1_54
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DOI: https://doi.org/10.1007/978-3-319-11197-1_54
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11196-4
Online ISBN: 978-3-319-11197-1
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