  Cyber-physical systems posit a complex number of security challenges due to
interconnection of heterogeneous devices having limited processing,
communication, and power capabilities. Additionally, the conglomeration of both
physical and cyber-space further makes it difficult to devise a single security
plan spanning both these spaces. Cyber-security researchers are often
overloaded with a variety of cyber-alerts on a daily basis many of which turn
out to be false positives. In this paper, we use machine learning and natural
language processing techniques to predict the consequences of cyberattacks. The
idea is to enable security researchers to have tools at their disposal that
makes it easier to communicate the attack consequences with various
stakeholders who may have little to no cybersecurity expertise. Additionally,
with the proposed approach researchers' cognitive load can be reduced by
automatically predicting the consequences of attacks in case new attacks are
discovered. We compare the performance through various machine learning models
employing word vectors obtained using both tf-idf and Doc2Vec models. In our
experiments, an accuracy of 60% was obtained using tf-idf features and 57%
using Doc2Vec method for models based on LinearSVC model.
