# Two-step Automated Cybercrime Coded Word Detection using Multi-level Representation Learning

arXiv: https://arxiv.org/abs/2403.10838 · HTML: https://mlsanity.com/paper/2403.10838/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 20 references that are in the map.
- First posted on arXiv 16 Mar 2024.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Self-Supervised Euphemism Detection and Identification for Content Moderation](https://mlsanity.com/paper/2103.16808/) (2021)
- [DarkBERT: A Language Model for the Dark Side of the Internet](https://mlsanity.com/paper/2305.08596/) (2023)
- [The Dark Side of the Language: Pre-trained Transformers in the DarkNet](https://mlsanity.com/paper/2201.05613/) (2022)
- [The Language of Legal and Illegal Activity on the Darknet](https://mlsanity.com/paper/1905.05543/) (2019)
- [Unsupervised Cyberbullying Detection via Time-Informed Gaussian Mixture Model](https://mlsanity.com/paper/2008.02642/) (2020)
- [Shedding New Light on the Language of the Dark Web](https://mlsanity.com/paper/2204.06885/) (2022)
- [Structure and Content of the Visible Darknet](https://mlsanity.com/paper/1811.01348/) (2018)

## Abstract (arXiv)

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.