New patent publication that seeks to cover techniques we developed for categorising different actors on a blockchain using vectors built from neighbourhood characteristics.
A computer implemented method of anomalous behaviour detection of an entity transacting in a distributed transactional database, the method comprising: selecting a subset of features of at least a first subset of transactions in the database as a feature set; generating a statistical model of the first subset of transactions in terms of the selected features; identifying a second subset of transactions in the database comprising transactions related to the entity; generating an encoded representation of each transaction in the second subset based on a comparison of the selected features of the transaction with the statistical model, such that the encoded representation of at least some of the transactions in the second subset identify behaviour of the entity as anomalous.
The table below defines, by way of example only, an ordered feature set in which earlier features are prioritised as more significant. An exemplary description of each feature and a suggestion of what each feature might indicate is also provided:
Full publication on WIPO: WO2020144021