A stability-validated unsupervised anomaly detection framework for identifying suspicious pre-listing trading behavior in cryptocurrency markets

Abstract Insider trading detection in cryptocurrency markets is challenging due to the absence of ground truth labels and the introduced pseudonymity. Prior work has focused on rule-based methods, as well as investigated lower-volume data, with limited validation. This work proposes an unsupervised anomaly detection framework validated by model stability. Using public on-chain decentralized exchange trading data, an Isolation Forest was employed and compared across stochastic runs. Given the stability validation, the event specificity of the pre-announcement window and feature representations are investigated. This serves as a case study of the ASTER token and its Coinbase exchange listing. Results show the stability of the produced anomaly structure across feature sets differs significantly. Network-based features produced the highest stability. Stability and event specificity were found to represent distinct properties, as the most stable feature representations were not necessarily event specific. The best performing feature representations across both validation dimensions were the combination of network and behavioural-change features. These findings were consistent under fixed and temporally matched baseline designs. The results suggest that unsupervised anomaly detection, coupled with stability-based validation, provides a practical tool for assessing potential insider trading risk, applicable for estimating Web3 project sustainability.