ABSTRACT :  Network traffic analysis, with the objective of identifying and preempting malicious campaigns, is an active area of research. An effective model that predicts future malicious network events based on observed malicious event sequences can aid with preemptive action that includes intervention by a security analyst. Predicting threat events that are part of a cybersecurity threat campaign that spans a long duration of time remains a challenge as the time lag between various steps in a campaign is unbounded. In this paper, we describe an approach to create an ensemble of Hidden Markov Models trained on sequences of malicious network events. The ensemble is used to predict the next expected malicious event given an already observed malicious traffic sequence at any network host. Ensembles of different sizes in combination with two prediction strategies are evaluated using prediction accuracy relative to two baselines predictors.