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The influenza-A virus is a rapidly mutating virus. Detecting the variability of the influenza-A virus is a key point in understanding the changeability in the genetic information of this virus and can help in designing or rebuilding the annual vaccine. In this paper, we propose a novel distance distribution paradigm to detect influenza-virus variability in high dimensional spaces. We conduct an experiment and the results show the robustness of the proposed paradigm in detecting this variability.