On-Chain Transaction Anomaly Detection Using BlockSecAnalyzer for Blockchain Forensics

Abstract

Blockchain ecosystems require continuous surveillance to detect anomalous on-chain transactions and mitigate illicit activities such as fraud, money laundering, and unauthorized smart contract interactions. This study presents BlockSecAnalyzer, an analytical framework designed for on-chain transaction anomaly detection to support blockchain forensics. The proposed approach analyzes transaction patterns to identify deviations from normal behavior, including abnormal transaction volumes, rapid fund movements, and atypical smart contract interactions. BlockSecAnalyzer integrates machine learning techniques with statistical modeling to generate transaction-level alerts and actionable forensic insights. Real-time monitoring enables early identification of suspicious activities, while continuous analysis supports proactive threat mitigation strategies. Experimental evaluation demonstrates the effectiveness of the proposed framework in enhancing the security and resilience of blockchain networks against fraudulent and malicious operations.