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Abusive traffic caused by worms is increasing severely in the Internet. In many cases, worm-infected hosts generate a huge number of flows of small size during a short time. To suppress the abusive traffic and prevent worms from spreading, identifying these "superspreaders" as soon as possible and coping with them, e.g, disconnecting them from the network, is important. This paper proposes a simple and adaptive method of identifying superspreaders by flow sampling. By satisfying the given memory size and the requirement for the processing time, the proposed method can adaptively optimize parameters according to changes in traffic patterns.