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.

翻译：暗方言是隐蔽的、阴险的、为地下论坛参与者用于非法行为的良性、看似友好的词语。 例如,暗方言“ 鼠” 常常用来代替“ 远程访问特洛伊” 。 在这项工作中,我们提出了一个自动识别和解释暗方言的新颖方法。 我们把问题正式化为从暗方言到“ 干净” 的绘图,而没有隐蔽的意思。 我们的方法使用以共享词汇的概率分布为形式的暗方言的可解释表达方式。 在我们的实验中,我们用暗方言识别方法显示我们的方法在暗方言识别方面是有效的,因为它超越了模拟数据上的另一个相关方法。 我们通过人工评估, 显示我们的方法能够在现实世界的地下论坛数据集中探测暗方言。