  Dark jargons are benign-looking words that have hidden, sinister meanings and
are used by participants of underground forums for illicit behavior. For
example, the dark term "rat" is often used in lieu of "Remote Access Trojan".
In this work we present a novel method towards automatically identifying and
interpreting dark jargons. We formalize the problem as a mapping from dark
words to "clean" words with no hidden meaning. Our method makes use of
interpretable representations of dark and clean words in the form of
probability distributions over a shared vocabulary. In our experiments we show
our method to be effective in terms of dark jargon identification, as it
outperforms another related method on simulated data. Using manual evaluation,
we show that our method is able to detect dark jargons in a real-world
underground forum dataset.
