EUREKHA: Enhancing User Representation for Key Hackers Identification in Underground Forums

TL;DR

EUREKHA is a novel approach that combines large language models, graph neural networks, and topic modeling to accurately identify key hackers in underground forums, significantly outperforming existing methods.

Contribution

The paper introduces EUREKHA, a new method integrating LLMs, GNNs, and topic modeling for improved key hacker identification in underground forums.

Findings

Fine-tuned LLMs outperform state-of-the-art methods.

Combining LLMs with GNNs improves accuracy and F1-score.

EUREKHA achieves 6-10% performance gains over existing approaches.

Abstract

Underground forums serve as hubs for cybercriminal activities, offering a space for anonymity and evasion of conventional online oversight. In these hidden communities, malicious actors collaborate to exchange illicit knowledge, tools, and tactics, driving a range of cyber threats from hacking techniques to the sale of stolen data, malware, and zero-day exploits. Identifying the key instigators (i.e., key hackers), behind these operations is essential but remains a complex challenge. This paper presents a novel method called EUREKHA (Enhancing User Representation for Key Hacker Identification in Underground Forums), designed to identify these key hackers by modeling each user as a textual sequence. This sequence is processed through a large language model (LLM) for domain-specific adaptation, with LLMs acting as feature extractors. These extracted features are then fed into a Graph Neural Network (GNN) to model user structural relationships, significantly improving identification accuracy. Furthermore, we employ BERTopic (Bidirectional Encoder Representations from Transformers Topic Modeling) to extract personalized topics from user-generated content, enabling multiple textual representations per user and optimizing the selection of the most representative sequence. Our study demonstrates that fine-tuned LLMs outperform state-of-the-art methods in identifying key hackers. Additionally, when combined with GNNs, our model achieves significant improvements, resulting in approximately 6% and 10% increases in accuracy and F1-score, respectively, over existing methods. EUREKHA was tested on the Hack-Forums dataset, and we provide open-source access to our code.