Data is the currency of the digital age. It is the foundation for analytics. The value of data lies in the context it provides and the timeliness of its content. Information decline is an important concern for data scientists in predictive security analytics. Using risk scores effectively, data decay can be mitigated.
Risk Scoring Transactions
The riskiness of a transaction, or a user’s activity, is often assessed in security analytics to detect a threat, or to prevent an attack. Predictive modeling uses a set of well-known analytical techniques applied to the cyber security domain to risk score event based transactions. These scores convey a sense of “riskiness” for that event at a transactional level but does not capture user intent. To quantify user behavior, there is a need for aggregating event-based scores from various models. The risk score will then be the summary of multiple scores obtained from various individual scoring algorithms within a system.
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