Dynamic Insider Threat Detection Based on Adaptable Genetic Programming
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gp-bibliography.bib Revision:1.7964
- @InProceedings{Le:2019:SSCI,
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author = "Duc C. Le and A. Nur Zincir-Heywood and
Malcolm I. Heywood",
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booktitle = "2019 IEEE Symposium Series on Computational
Intelligence (SSCI)",
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title = "Dynamic Insider Threat Detection Based on Adaptable
Genetic Programming",
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year = "2019",
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pages = "2579--2586",
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month = dec,
-
keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/SSCI44817.2019.9003134",
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abstract = "Different variations in deployment environments of
machine learning techniques may affect the performance
of the implemented systems. The variations may cause
changes in the data for machine learning solutions,
such as in the number of classes and the extracted
features. This paper investigates the capabilities of
Genetic Programming (GP) for malicious insider
detection in corporate environments under such changes.
Assuming a Linear GP detector, techniques are
introduced to allow a previously trained GP population
to adapt to different changes in the data. The
experiments and evaluation results show promising
insider threat detection performances of the techniques
in comparison with training machine learning
classifiers from scratch. This reduces the amount of
data needed and computation requirements for obtaining
dependable insider threat detectors under new
conditions.",
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notes = "Also known as \cite{9003134}",
- }
Genetic Programming entries for
Duc C Le
Nur Zincir-Heywood
Malcolm Heywood
Citations