International Business Machines Corporation

Paul J Dwyer

Aankur Bhatia

Gary I Givental

Yiye Huang

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.

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, a threat disposition score (TDS) is retrieved. The threat disposition score 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 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. Depending on the TDS (and its confidence level), the analyst may be able to respond to the threat immediately, i.e., without further detailed investigation. Preferably, the machine learning model is updated continuously as the system handles security threats, thereby increasing the predictive benefit of the TDS scoring.

Mechanisms for performing advanced rule analysis are provided. The mechanisms perform natural language processing of a security rule set data structure, specifying a plurality of security rules. The mechanisms execute, for each security rule pairing, a determination of a similarity measure indicating a degree of similarity of the textual description of the first security rule in the pairing with the textual description of the second security rule in the pairing, and in response to the security measure being equal to or above duplicate rule threshold value, eliminating one of the first security rule or the second security rule in the pairing from the security rule set data structure to generate a modified security rule set data structure. The mechanisms deploy the modified security rule set data structure to a computing environment for use in identifying security incidents and performing event management.

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Pewaukee, WI, United States of America

Paul J Dwyer