Every day numerous new vulnerabilities and exploits are reported for a wide variety of different software configurations. There is a big need to be able to quickly assess associated risks and sort out which vulnerabilities that are likely to be exploited in real-world attacks. A small percentage of all vulnerabilities account for almost all the observed attack volume. We use machine learning to make automatic predictions for unseen vulnerabilities based on previous exploit patterns.

An approach to computational vision that is based on multiple levels of interpretation is presented. The step between each level is seen as taking place in three stages-parsing (in which features and groups of features in an image are given labels), interpreting (in which several interpretations are built, assuring that each feature is given at most one explanation in terms of a higher-level label), and pruning (in which some interpretations are discarded because of global constraints). The parsing and pruning steps are guided by multirelational grammars, a generalization of ordinary attribute grammars and of graph grammars. A bottom-up parsing algorithm for this class of grammars is presented, and their usefulness in image interpretation is illustrated by examples using both synthetic and real-world data.>