Threat disposition analysis and modeling using supervised machine learning
An enhanced threat disposition analysis technique is provided. In response to receipt of a security threat identified in an alert, a threat disposition score (TDS) is retrieved. The TDS is generated from a machine learning scoring model that is built from information about historical security threats, including historical disposition of one or more alerts associated with the historical security threats. The TDS is based in part on an effectiveness of a prior calculated TDS to predict a particular historical disposition associated with the alert. The system augments an alert to include the threat disposition score, optionally together with a confidence level, to generate an enriched alert. The enriched alert is then presented to the security analyst for handling directly. Preferably, the machine learning model is updated continuously as the system handles security threats, thereby increasing the predictive benefit of the TDS scoring.
1. A method for threat disposition analysis, comprising:
responsive to receipt of a security threat identified in an alert, retrieving a threat disposition score (TDS), the threat disposition score generated from a machine learning scoring model built from information about historical security threats, including historical disposition of one or more alerts associated with the historical security threats, the TDS based in part on an effectiveness of a prior calculated TDS to predict a particular historical disposition associated with the alert; and
augmenting the alert to include the threat disposition score to generate an enriched alert.
2. The method as described in claim 1 wherein the enriched alert also includes historical information about how the security threat has been handled previously.
3. The method as described in claim 1 wherein the historical disposition comprises one of: an action taken by a security analyst in response to an alert associated with the historical security threat, and feedback on handling of the alert associated with the historical security threat.
4. The method as described in claim 1 further including building the machine learning scoring model, wherein the machine learning scoring model also is built from a set of attributes regarding an alert.
5. The method as described in claim 4 further including receiving data configuring the set of attributes.
6. The method as described in claim 1 further including updating the machine learning scoring model.
7. The method as described in claim 1 , further comprising:
providing a confidence level associated with the TDS; and
responsive to the confidence level reaching a threshold, automatically performing a set of one or more actions to respond to the security threat.
8. The method as described in claim 1 wherein the further handling is one of: closing the security threat as a false positive, and escalating the security threat.
9. An apparatus, comprising:
a processor;
computer memory holding computer program instructions executed by the processor for threat disposition analysis, the computer program instructions operative to:
retrieve a threat disposition score (TDS) in response to receipt of a security threat identified in an alert, the threat disposition score generated from a machine learning scoring model built from information about historical security threats, including historical disposition of one or more alerts associated with the historical security threats, the TDS based in part on an effectiveness of a prior calculated TDS to predict a particular historical disposition associated with the alert; and
augment the alert to include the threat disposition score to generate an enriched alert.
10. The apparatus as described in claim 9 wherein the enriched alert also includes historical information about how the security threat has been handled previously.
11. The apparatus as described in claim 9 wherein the historical disposition comprises one of: an action taken by a security analyst in response to an alert associated with the historical security threat, and feedback on handling of the alert associated with the historical security threat.
12. The apparatus as described in claim 9 wherein the computer program instructions are further operative to build the machine learning scoring model, wherein the machine learning scoring model also is built from a set of attributes regarding an alert.
13. The apparatus as described in claim 12 wherein the computer program instructions also are operative to receive data configuring the set of attributes.
14. The apparatus as described in claim 9 wherein the computer program instructions also are operative to update the machine learning scoring model.
15. The apparatus as described in claim 9 wherein the computer program instructions also are operative to:
provide a confidence level associated with the TDS; and
responsive to the confidence level reaching a threshold, automatically perform a set of one or more actions to respond to the security threat.
16. The apparatus as described in claim 9 wherein the further handling is one of: closing the security threat as a false positive, and escalating the security threat.
17. A computer program product in a computer readable storage device for use in a data processing system for threat disposition analysis, the computer program product holding computer program instructions that, when executed by the data processing system, are operative to:
retrieve a threat disposition score (TDS) in response to receipt of a security threat identified in an alert, the threat disposition score generated from a machine learning scoring model built from information about historical security threats, including historical disposition of one or more alerts associated with the historical security threats, the TDS based in part on an effectiveness of a prior calculated TDS to predict a particular historical disposition associated with the alert; and
augment the alert to include the threat disposition score to generate an enriched alert.
18. The computer program product as described in claim 17 wherein the enriched alert also includes historical information about how the security threat has been handled previously.
19. The computer program product as described in claim 17 wherein the historical disposition comprises one of: an action taken by a security analyst in response to an alert associated with the historical security threat, and feedback on handling of the alert associated with the historical security threat.
20. The computer program product as described in claim 17 wherein the computer program instructions are further operative to build the machine learning scoring model, wherein the machine learning scoring model also is built from a set of attributes regarding an alert.
21. The computer program product as described in claim 20 wherein the computer program instructions also are operative to receive data configuring the set of attributes.
22. The computer program product as described in claim 17 wherein the computer program instructions also are operative to update the machine learning scoring model.
23. The computer program product as described in claim 17 wherein the computer program instructions also are operative to:
provide a confidence level associated with the TDS; and
responsive to the confidence level reaching a threshold, automatically perform a set of one or more actions to respond to the security threat.
24. The computer program product as described in claim 17 wherein the further handling is one of: closing the security threat as a false positive, and escalating the security threat.
25. A security threat analysis platform, comprising:
one or more hardware processors;
computer memory storing computer program instructions configured to:
retrieve a threat disposition score (TDS) in response to receipt of a security threat identified in an alert, the threat disposition score generated from a machine learning scoring model built from information about historical security threats, including historical disposition of one or more alerts associated with the historical security threats, the TDS based in part on an effectiveness of a prior calculated TDS to predict a particular historical disposition associated with the alert; and
augment the alert to include the threat disposition score to generate an enriched alert.