Summary of Enhanced Online Grooming Detection Employing Context Determination and Message-level Analysis, by Jake Street et al.
Enhanced Online Grooming Detection Employing Context Determination and Message-Level Analysis
by Jake Street, Isibor Ihianle, Funminiyi Olajide, Ahmad Lotfi
First submitted to arxiv on: 12 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed research introduces a novel approach to detecting Online Grooming (OG) attacks on social media/messaging platforms. The authors recognize that existing solutions focus on signature analysis of child abuse media, which is inadequate for real-time OG detection. Instead, they propose identifying specific communication patterns between adults and children as a key factor in detecting these complex attacks. The novel approach leverages advanced models such as BERT and RoBERTa for Message-Level Analysis and Context Determination to classify actor interactions. This includes 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 this approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Online Grooming (OG) is a serious threat where adults prey on children online, causing severe psychological and physical harm. Current methods to detect OG are not effective, especially with end-to-end encryption making it harder to monitor messages. The authors suggest that OG attacks are complex and require identifying specific communication patterns between adults and children. They propose a new method using advanced AI models like BERT and RoBERTa to analyze messages and determine the significance of interactions. This could help improve detection accuracy and prevent more attacks. |
Keywords
» Artificial intelligence » Bert
