As the metaverse gains traction, the importance of metaverse security research becomes increasingly evident. While there has been research on authenticating users in the metaverse, there is a notable gap in research concerning the authentication of specific spaces within the metaverse. This paper addresses this gap by proposing a novel user-centric blockchain-based authentication approach that incorporates space authentication. The proposed approach leverages blockchain smart contracts to authenticate users using cosine similarity metrics. A significant advantage of this approach its ability to establish user-centric authentication by seamlessly integrating metaverse and blockchain technologies, all without the need for a centralized authority. In this paper, we not only evaluate the security of our proposed approach but also conduct experiments to determine the cosine similarity threshold and assess its feasibility within a metaverse environment.

Illicit online communication, such as drug-dealing dialogues, is increasingly conducted through covert, context dependent language patterns that evade traditional detection techniques in South Korea. However, developing reliable AI based detection systems remains challenging due to the scarcity of real world training data in such sensitive domains. This paper proposes a novel persona-driven data augmentation framework using Large Language Model(LLM) to generate realistic synthetic drug-dealing dialogues. By encoding domain specific buyer and seller personas along with linguistic behaviour rules, the method produces contextually coherent and semantically diverse dialogues that reflect authentic communication styles. Evaluation results demonstrate that the augmented data preserves key stylistic features (high cosine similarity), maintains lexical diversity (TTR), improves fluency (perplexity), and enhances coherence and lexical richness (ROUGE-L), outperforming traditional augmentation method. Furthermore, statistical validation confirms the semantic consistency and stability of the generated data. These findings highlight the viability of LLM-based augmentation in low-resource, high-risk domains and suggest its potential transferability to other specialized NLP applications requiring context-preserving generation.

The rise of drug‑related crime in South Korea, especially via online messengers, reveals clear limits in keyword‑based or network‑tracking detection methods. To address this in a low‑resource setting, we propose a large‑language‑model (LLM) persona‑driven data‑augmentation framework. Buyer and seller personas replicate authentic linguistic patterns, slang and delivery practices, generating realistic, context‑rich dialogue. Using text‑embedding similarity, type–token ratio (TTR), perplexity, dialogue coherence and ROUGE, we show that 15 000 augmented dialogues closely mirror 87 real conversations while boosting lexical variety and contextual consistency. Results confirm that persona‑driven augmentation mitigates data scarcity and improves illicit‑dialogue detectors, offering a transferable strategy for other sensitive, low‑data domains such as voice phishing or fraudulent trade.