International Journal of Advanced Research in Computer and Communication Engineering

Abstract: The increasing reliance on web-based transaction systems has intensified security threats related to anomalous transaction behaviors that may indicate cyberattacks, fraud, or policy violations. Conventional detection mechanisms, such as regex-based filtering and manually defined rule-based methods, are widely used but often suffer from high false positive rates, limited adaptability, and performance instability as transaction patterns evolve. Learning-based approaches offer adaptability but introduce challenges related to explainability, training data dependency, and computational overhead, which limit their suitability for audit-oriented security environments. To address these limitations, this study proposes an automata-based model for transaction anomaly detection integrated with blockchain-based digital evidence storage. The proposed approach models valid web transaction syntax using deterministic finite automata (DFA), enabling transparent, rule-driven anomaly detection without reliance on training data. Blockchain technology is employed as an immutable logging layer to preserve digital evidence of detected anomalies, ensuring integrity, traceability, and auditability. The model is evaluated using simulated HTTP transaction datasets in a local server environment and benchmarked against regex-based and manual rule-based detection methods. Performance evaluation focuses on detection accuracy, false positive rate, execution time, and blockchain overhead in terms of latency and storage consumption. Experimental results demonstrate that the DFA-based model achieves higher detection accuracy, lower false positive rates, and more stable execution times than baseline approaches. Although blockchain integration introduces additional overhead, the impact remains predictable and manageable. Overall, the results indicate that combining automata-based detection with blockchain-based evidence storage provides an effective, explainable, and trustworthy solution for secure web transaction monitoring.

Keywords: anomaly detection; automata-based detection; blockchain evidence storage; transaction security; web transactions.

Downloads: | DOI: 10.17148/IJARCCE.2026.15101