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- W4367671105 abstract "The remarkable ability to separate and identify self and non-self in a given problem space, makes negative selection a fascinating concept of artificial immune system. Therefore, negative selection has attracted research interest and is studied and explored for complex problem solving across different application areas. Anomaly detection in computer security is a thriving area of research and has witnessed various new explores involving different computational intelligence techniques. Negative selection with its core ability to detect self and non-self along with traits like adaptability, learning, robustness and faster response makes it a suitable and compelling concept for anomaly detection. Over the years, negative selection has evolved from its preliminary theories and it has embraced new improvements from computational intelligence in its several concepts. This paper intends to review various negative selection taxonomies, representations and matching techniques from inception to current scenario in anomaly detection. It attempts to critically evaluate and classify available literature to establish future areas of research for formulating potential solutions to mitigate the complex security challenges." @default.
- W4367671105 created "2023-05-03" @default.
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- W4367671105 date "2023-05-01" @default.
- W4367671105 modified "2023-10-16" @default.
- W4367671105 title "Negative selection in anomaly detection—A survey" @default.
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- W4367671105 doi "https://doi.org/10.1016/j.cosrev.2023.100557" @default.
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