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There is significant interest to develop proactive approaches to cyber defense, in which future attack strategies are anticipated and these insights are incorporated into defense designs. This paper considers the problem of protecting computer networks against intrusions and other attacks, and leverages the coevolutionary relationship between attackers and defenders to derive two new methods for proactive network defense. The first method is a bipartite graph-based machine learning algorithm which enables information concerning previous attacks to be “transferred” for application against novel attacks, thereby substantially increasing the rate with which defense systems can successfully respond to new attacks. The second approach involves exploiting basic threat information (e.g., from cyber security analysts) to generate “synthetic” attack data for use in training defense systems, resulting in networks defenses that are effective against both current and (near) future attacks. The utility of the proposed methods is demonstrated by showing that they outperform standard techniques for the task of detecting malicious network activity in two publicly-available cyber datasets.