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Prevention is normally recognized as one of the best defense strategy against malicious hackers or attackers. The desire of deploying better prevention mechanisms has motivated many security researchers and practitioners, who are studies threat trend analysis models. However, threat trend is not directly revealed from the time-series data because the trend is implicit in its nature. Besides, traditional time-series analysis, which predicts the future trend pattern by relying exclusively on the past trend pattern, is not appropriate for predicting a trend pattern in dynamic network environments (e.g., the Internet). Thus, supplemental environmental information is required to uncover a trend pattern from the implicit (or hidden) raw data. In this paper, we propose cyber threat trend analysis model using hidden Markov model (HMM) by incorporating the supplemental environmental information into the trend analysis.