Enhanced Online Grooming Detection Employing Context Determination and Message-Level Analysis

TL;DR

This paper presents a novel approach for online grooming detection that combines message-level analysis with context determination using advanced NLP models like BERT and RoBERTa, aiming to improve accuracy in real-time scenarios.

Contribution

It introduces a new method integrating context analysis and message-level features with thresholds, enhancing detection of grooming behaviors over existing signature-based approaches.

Findings

Improved detection accuracy across multiple datasets

Effective identification of communication patterns

Robustness against encrypted messaging environments

Abstract

Online Grooming (OG) is a prevalent threat facing predominately children online, with groomers using deceptive methods to prey on the vulnerability of children on social media/messaging platforms. These attacks can have severe psychological and physical impacts, including a tendency towards revictimization. Current technical measures are inadequate, especially with the advent of end-to-end encryption which hampers message monitoring. Existing solutions focus on the signature analysis of child abuse media, which does not effectively address real-time OG detection. This paper proposes that OG attacks are complex, requiring the identification of specific communication patterns between adults and children. It introduces a novel approach leveraging advanced models such as BERT and RoBERTa for Message-Level Analysis and a Context Determination approach for classifying actor interactions, including the introduction of Actor Significance Thresholds and Message Significance Thresholds. The proposed method aims to enhance accuracy and robustness in detecting OG by considering the dynamic and multi-faceted nature of these attacks. Cross-dataset experiments evaluate the robustness and versatility of our approach. This paper's contributions include improved detection methodologies and the potential for application in various scenarios, addressing gaps in current literature and practices.