Classifying cybergrooming for child online protection using hybrid machine learning model

Abstract

This paper shows a computational model that classifies Cybergrooming attacks in the context of COP (child online protection) using Natural Language Processing (NLP) and Convolutional Neural Networks (CNN). The model predicts a high number of false positives, therefore low precision and F-score, but a high accuracy. In this issue, where the number of messages in the context of grooming are so low compared to the number of conversations and messages from other contexts, it can be concluded that is a very consistent and useful result as it captures a high number of true positives, considering that the classifier works for messages. Performing the training of machine learning algorithms with neural networks, semantic analysis and NLP, allows approximate representation of knowledge contributing to discovery of pseudo-intelligent information in these environments and reducing human intervention for characterization of underlying abnormal behavior and detecting messages that potentially represent these attacks.