The rise of blockchain technology and cryptocurrencies such as Bitcoin and Ethereum has created new avenues for both lawful and illicit activities, including illegal firearm transactions. This study applies a combination of graph-based analysis, functional data techniques, and machine learning to detect and classify suspicious activities related to firearm trafficking on blockchain networks. A Random Forest model, achieving a precision of 0.907 and recall of 0.786, was used to identify illicit Bitcoin addresses, while a multi-target classifier categorized these addresses by specific types of illicit activity. For Ethereum, an XGBoost model achieved a precision of 0.9864 and an accuracy of 0.9901, demonstrating robust detection of suspicious accounts. Feature engineering and a rule-based system further enhanced model performance, though challenges remain in addressing misclassifications, particularly in distinguishing subtle transaction patterns. These findings underscore the potential of machine learning in blockchain forensics, providing critical insights for law enforcement efforts to combat illegal firearm trading.

This research delves into the dual nature of cryptocurrencies, offering financial opportunities while addressing the surge in digital criminal activities, especially in illegal firearms trafficking. The decentralized nature of blockchain technology presents unique challenges for law enforcement, necessitating innovative approaches to uncover and prevent criminal transactions. The study utilizes advanced data mining, analytics techniques, and machine learning models to analyze transactional graphs of prominent cryptocurrencies, aiming to identify and thwart transactions linked to illegal firearms trafficking.Additionally, the paper provides an overview of the current state of blockchain technology research and introduces the ambitious Ceasefire project. This initiative outlines a systematic approach to combat illegal firearms trading within the cryptocurrency domain, leveraging cutting-edge techniques and strategic partnerships with leading blockchain analysis platforms.By proposing a novel method, this paper enhances the ability to detect illicit firearms trading in cryptocurrencies, specifically focusing on Bitcoin and Ethereum networks. The approach combines predictive modeling with rule-based matching to identify potentially suspicious addresses in both ecosystems. This empowers authorities to track individuals attempting to conceal their transactional activities by transitioning between Bitcoin and Ethereum, thus bolstering efforts to maintain the integrity of decentralized financial systems.