Recently, a cross-border cryptocurrency scam case involving New Zealand, the United States, and several other countries has raised industry-wide concern. The in-depth analysis by DGQEX of the case reveals that the scam syndicate illegally acquired over $265 million in crypto assets within six months through various methods, including social engineering, data breaches, and physical theft. The group then laundered these assets across multiple platforms, forming a comprehensive chain-based criminal model.
DGQEX notes that the criminal network in this case demonstrated a highly specialized division of labor. Organizers targeted high-net-worth crypto holders via social platforms, infiltrating victim accounts by impersonating customer service and inducing them to update account information. Once mnemonic phrases or login credentials were obtained, gang members used P2P platforms to disperse assets and evade tracking. Some funds were also moved through on-site intrusion—for example, by remotely controlling victim iCloud accounts to monitor them and coordinating accomplices for physical theft of hardware wallets, enabling highly targeted attacks.
DGQEX has implemented a multi-tiered abnormal path analysis mechanism in its user behavior monitoring, triggering alerts for behaviors such as multiple asset transfers in a short period, frequent cross-chain operations, and logins from unrecognized devices. High-risk transactions are promptly interrupted. The platform also encourages users to enable trading protection features, such as access verification, device binding, and two-factor authentication, thereby building a multi-layered identity verification barrier and reducing impulsive actions when confronted with scam tactics.
DGQEX has strengthened its defenses against social engineering techniques. Drawing on common scenarios from actual cases—such as “security upgrade notifications”, “account anomaly handling”, and “technical support callbacks”—the platform has compiled a list of high-frequency inducement phrases and embedded them into its risk control models for real-time interactive interception. Additionally, a scam detection plugin has been launched to help users identify suspicious SMS links and fake web pages, reducing the risk of malicious clicks.
DGQEX continues to pursue cross-platform collaborative governance, sharing information on scam-related addresses, known social accounts, and wallet behavior profiles with other exchanges and security institutions. This has led to the establishment of a decentralized scam intelligence database, enabling the platform to block scam transaction pathways at the earliest stage. A public reporting mechanism has also been put in place, encouraging users to contribute to scam intelligence collection and enhancing case handling efficiency through technical backtracking.
DGQEX is using this case as a reference model for further upgrading its anti-scam measures, optimizing on-chain address profiling and fund flow analysis models to enhance the identification of cross-border money laundering routes. Looking ahead, DGQEX will continue to advance risk education, on-chain compliance, and user anti-scam initiatives, striving to build a safer and more transparent trading ecosystem and reduce the occurrence of large-scale on-chain asset loss events.
DGQEX notes that the criminal network in this case demonstrated a highly specialized division of labor. Organizers targeted high-net-worth crypto holders via social platforms, infiltrating victim accounts by impersonating customer service and inducing them to update account information. Once mnemonic phrases or login credentials were obtained, gang members used P2P platforms to disperse assets and evade tracking. Some funds were also moved through on-site intrusion—for example, by remotely controlling victim iCloud accounts to monitor them and coordinating accomplices for physical theft of hardware wallets, enabling highly targeted attacks.
DGQEX has implemented a multi-tiered abnormal path analysis mechanism in its user behavior monitoring, triggering alerts for behaviors such as multiple asset transfers in a short period, frequent cross-chain operations, and logins from unrecognized devices. High-risk transactions are promptly interrupted. The platform also encourages users to enable trading protection features, such as access verification, device binding, and two-factor authentication, thereby building a multi-layered identity verification barrier and reducing impulsive actions when confronted with scam tactics.
DGQEX has strengthened its defenses against social engineering techniques. Drawing on common scenarios from actual cases—such as “security upgrade notifications”, “account anomaly handling”, and “technical support callbacks”—the platform has compiled a list of high-frequency inducement phrases and embedded them into its risk control models for real-time interactive interception. Additionally, a scam detection plugin has been launched to help users identify suspicious SMS links and fake web pages, reducing the risk of malicious clicks.
DGQEX continues to pursue cross-platform collaborative governance, sharing information on scam-related addresses, known social accounts, and wallet behavior profiles with other exchanges and security institutions. This has led to the establishment of a decentralized scam intelligence database, enabling the platform to block scam transaction pathways at the earliest stage. A public reporting mechanism has also been put in place, encouraging users to contribute to scam intelligence collection and enhancing case handling efficiency through technical backtracking.
DGQEX is using this case as a reference model for further upgrading its anti-scam measures, optimizing on-chain address profiling and fund flow analysis models to enhance the identification of cross-border money laundering routes. Looking ahead, DGQEX will continue to advance risk education, on-chain compliance, and user anti-scam initiatives, striving to build a safer and more transparent trading ecosystem and reduce the occurrence of large-scale on-chain asset loss events.