Two-step Automated Cybercrime Coded Word Detection using Multi-level
  Representation Learning
Yongyeon Kim Byung-Won On Ingyu Lee
In social network service platforms, crime suspects are likely to use
cybercrime coded words for communication by adding criminal meanings to
existing words or replacing them with similar words. For instance, the word
'ice' is often used to mean methamphetamine in drug crimes. To analyze the
nature of cybercrime and the behavior of criminals, quickly detecting such
words and further understanding their meaning are critical. In the automated
cybercrime coded word detection problem, it is difficult to collect a
sufficient amount of training data for supervised learning and to directly
apply language models that utilize context information to better understand
natural language. To overcome these limitations, we propose a new two-step
approach, in which a mean latent vector is constructed for each cybercrime
through one of five different AutoEncoder models in the first step, and
cybercrime coded words are detected based on multi-level latent representations
in the second step. Moreover, to deeply understand cybercrime coded words
detected through the two-step approach, we propose three novel methods: (1)
Detection of new words recently coined, (2) Detection of words frequently
appeared in both drug and sex crimes, and (3) Automatic generation of word
taxonomy. According to our experimental results, among various AutoEncoder
models, the stacked AutoEncoder model shows the best performance. Additionally,
the F1-score of the two-step approach is 0.991, which is higher than 0.987 and
0.903 of the existing dark-GloVe and dark-BERT models. By analyzing the
experimental results of the three proposed methods, we can gain a deeper
understanding of drug and sex crimes.