On-Chain Analysis
A technical field that analyzes publicly available blockchain transaction data to trace fund flows and identify entities and the identities of their controllers. It is commonly used in security incident investigations and compliance supervision.
Timeline (last 90 days)
The attribution clues given by on-chain analysts include similarities between the attack infrastructure's IP addresses and malware signatures and the known toolkits of North Korean hacking groups, as well as mixer usage habits appearing in the fund transfer paths.
The anonymity of on-chain data makes attribution work inherently error-prone, and attackers could entirely mimic the tactics of other groups to mislead investigations.
The loss amount in crypto asset theft cases usually can only be gradually confirmed after on-chain fund tracing, exchange audits and law enforcement involvement, and initial estimates often deviate.
On-chain analysis firms track the flow of stolen funds to addresses, mixer usage and cross-chain transfers through public blockchain ledgers, which can provide relatively objective evidence of fund paths but is difficult to convert directly into a platform's actual losses.
All Facts (4)
The attribution clues given by on-chain analysts include similarities between the attack infrastructure's IP addresses and malware signatures and the known toolkits of North Korean hacking groups, as well as mixer usage habits appearing in the fund transfer paths.
50%UnverifiedThe anonymity of on-chain data makes attribution work inherently error-prone, and attackers could entirely mimic the tactics of other groups to mislead investigations.
50%UnverifiedThe loss amount in crypto asset theft cases usually can only be gradually confirmed after on-chain fund tracing, exchange audits and law enforcement involvement, and initial estimates often deviate.
50%UnverifiedOn-chain analysis firms track the flow of stolen funds to addresses, mixer usage and cross-chain transfers through public blockchain ledgers, which can provide relatively objective evidence of fund paths but is difficult to convert directly into a platform's actual losses.
50%