Abdoul Amadou

Abdoul Nasser Hassane Amadou, Anas Motii, Saida Elouardi et al.

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.

Abdoul Nasser Hassane Amadou, Imane Fouad, Anas Motii

Cybercriminal underground forums enable anonymous collaboration, allowing users to trade illicit tools, discuss vulnerabilities, and distribute stolen data. Driven by shared interests and specialized skills, users on these platforms organize into distinct threat communities. However, identifying these communities is challenging due to their dynamic and opaque structures; traditional graph-based methods typically isolate dominant groups while overlooking smaller, hidden subgroups. This paper introduces HADES, an unsupervised framework designed to detect both dominant and hidden communities in underground forums. The framework models users based on their textual interactions and leverages pretrained language models to generate semantic embeddings that encode latent behavioral and thematic patterns. By clustering users based on semantic similarity and assigning topic labels to the resulting clusters, HADES identifies specific threat communities to support Cyber Threat Intelligence (CTI). The framework was evaluated on three major underground forums: HackForums, Cracked, and BreachForums. Results demonstrate that BERT embeddings consistently outperform alternative baselines, improving cluster coherence and achieving higher silhouette scores. Across these platforms, HADES identified dozens of distinct communities, effectively isolating small, specialized subgroups (fewer than 100 users) that evade traditional detection. Furthermore, because shared thematic interests frequently precede explicit structural connections, tracking these semantic patterns enables the framework to anticipate the formation of threat communities up to a year before they become detectable by traditional graph-based methods.