Machine learning model for calculating confidence scores associated with potential security vulnerabilities

A method comprising:

• implementing, using one or more hardware processors, a machine learning (ML) model for calculating confidence scores associated with potential security vulnerabilities detected via application security testing, wherein the implementing comprises:
• obtaining scan traffic data associated with one or more potential security vulnerabilities detected via one or more scan engines that implement application security testing, wherein the one or more scan engines send scan requests to different applications and detect potential security vulnerabilities using scan responses obtained in response to the scan requests;
• determining, using the ML model, respective confidence scores for each of the one or more potential security vulnerabilities, wherein the ML model is trained using vulnerability data associated with a set of previously identified vulnerabilities, and wherein the vulnerability data indicates, for each respective previously identified vulnerability of the set of previously identified vulnerabilities, whether the previously identified vulnerability is a true positive or a false positive;
• responsive to a request for scan findings associated with a particular application, displaying the respective confidence scores via a vulnerability analysis graphical user interface;
• and
• responsive to a selection of a particular potential security vulnerability via the vulnerability analysis graphical user interface, displaying a vulnerability details graphical user interface that indicates (a) a scan request that caused detection of the particular potential security vulnerability, (b) a scan response received in response to the scan request, (c) a module type of the particular potential security vulnerability detected, and (d) a confidence score determined by the ML model for the particular potential security vulnerability.

One or more non-transitory computer-accessible storage media storing program instructions that, when executed on or across one or more processors, implement at least a portion of a system that implements a machine learning (ML) model for calculating confidence scores associated with potential security vulnerabilities detected via application security testing and cause the system to:

• obtain scan traffic data associated with one or more potential security vulnerabilities detected via one or more scan engines that implement application security testing, wherein the one or more scan engines send scan requests to different applications and detect potential security vulnerabilities using scan responses obtained in response to the scan requests;
• determine, using the ML model, respective confidence scores for each of the one or more potential security vulnerabilities, wherein the ML model is trained using vulnerability data associated with a set of previously identified vulnerabilities, and wherein the vulnerability data indicates, for each respective previously identified vulnerability of the set of previously identified vulnerabilities, whether the previously identified vulnerability is a true positive or a false positive;
• responsive to a request for scan findings associated with a particular application, display the respective confidence scores via a vulnerability analysis graphical user interface;
• and
• responsive to a selection of a particular potential security vulnerability via the vulnerability analysis graphical user interface, display a vulnerability details graphical user interface that indicates (a) a scan request that caused detection of the particular potential security vulnerability, (b) a scan response received in response to the scan request, (c) a module type of the particular potential security vulnerability detected, and (d) a confidence score determined by the ML model for the particular potential security vulnerability.