Ranking cybersecurity alerts from multiple sources using machine learning
摘要:
The present disclosure relates to a machine-learning system, method, and computer program for ranking security alerts from multiple sources. The system self-learns risk levels associated with alerts by calculating risk probabilities for the alerts based on characteristics of the alerts and historical alert data. In response to receiving a security alert from one of a plurality of alert-generation sources, the alert-ranking system evaluates the security alert with respect to a plurality of feature indicators. The system creates a feature vector for the security alert based on the feature indicator values identified for the alert. The system then calculates a probability that the security alert relates to a cybersecurity risk in the computer network based on the created feature vector and historical alert data in the network. The system ranks alerts from a plurality of different sources based on the calculated cybersecurity risk probabilities.
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