Besides the node detection, traffic analysis serves as one of the critical tools for identifying anomalous behavior in the network. This is achieved by monitoring data flows and identifying patterns that deviate from established norms. Traffic-based anomaly detection (Chandola et al., ACM Comput Surv (CSUR) 41(3):1–58, 2009) is effective due to its ability to capture real-time network activity and spot irregularities, making it a valuable tool for detecting cyber threats, such as DDoS attacks, malware propagation, and unauthorized access attempts (Signorini et al., Bad: blockchain anomaly detection. arXiv preprint arXiv:180703833 (2018); Baqer et al., Financial Cryptography and Data Security: FC 2016 International Workshops, BITCOIN, VOTING, and WAHC, Christ Church, Barbados, February 26, 2016, Revised Selected Papers 20, Springer, pp 3–18, 2016). In traditional single-chain or standard network environments, traffic detection methods typically involve approaches like signature-based detection, statistical anomaly detection, machine learning-based methods, and behavioral analytics (Cup, http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html , The UCI KDD Archive, 1999). However, in the context of cross-chain systems, where multiple blockchains interact, traffic-based anomaly detection presents unique challenges. The heterogeneous and decentralized nature of cross-chain interactions requires the development of new approaches that can effectively analyze and identify anomalies across interconnected blockchain ecosystems, as traditional network monitoring methods may not directly apply (Moustafa and Slay, 2015 Military Communications and Information Systems Conference (MilCIS), IEEE, pp 1–6, 2015). Understanding existing techniques in single-chain networks is essential for adapting them to the more complex cross-chain environment.