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Cybercriminals exploit a large number of ephemeral malicious accounts for conducting large-scale simple attacks such as spam distribution on online social networks. However, conventional detection schemes relying on account or message information take a considerable time to collect such information before running detection algorithms so criminals utilize their accounts until suspension and exploit others again. In this paper, we propose a new detection scheme to filter potentially malicious account groups around their creation time. Our scheme utilizes the differences between algorithmically generated account names and human-made account names to identify malicious accounts generated using the similar algorithms. For accounts created within a short period of time, we apply a clustering algorithm to group accounts sharing similar name-based features and a classification algorithm to classify malicious …