Public disclosure of important security information, such as knowledge of vulnerabilities or exploits, often occurs in blogs, tweets, mailing lists, and other online sources significantly before proper classification into structured databases. In order to facilitate timely discovery of such knowledge, we propose a novel semi-supervised learning algorithm, PACE, for identifying and classifying relevant entities in text sources. The main contribution of this paper is an enhancement of the traditional bootstrapping method for entity extraction by employing a time-memory tradeoff that simultaneously circumvents a costly corpus search while strengthening pattern nomination, which should increase accuracy. An implementation in the cyber-security domain is discussed as well as challenges to Natural Language Processing imposed by the security domain.

Quantum data communications and networking involve classical hardware and software. Quantum storage is sensitive to environmental disturbances that may have malicious origins. Teleportation and entanglement swapping, two building blocks for the future quantum Internet, rely on secure classical bit communications. When lack of authenticity, integrity and replay protection may have a high impact, quantum data communications are at risk and need to be protected. Building upon quantum cryptography and random generation of quantum operators, we propose a solution to protect the authenticity, integrity and replay of quantum data communications. Our solution includes a classical data interface to quantum data cryptography. We describe how classical keying material can be mapped to quantum operators. This enables classical key management techniques for secure quantum data communications.

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