Abstract: A method for mitigating network abuse includes obtaining a first set of network traffic messages of network traffic currently received by a network service and determining, via a first model, whether network abuse is occurring based on the first set of network traffic messages. When the network abuse is occurring, the method includes obtaining a second set of current network traffic messages. The method also includes, for each network traffic message in the second set of network traffic messages, labeling, via a second model, the network traffic message as an abusing network traffic message or a non-abusing network traffic message. The method also includes generating, via a third model, at least one network traffic rule. Each network traffic rule, when implemented, reduces an effect of the abusing network traffic messages.

Abstract: A method for mitigating network abuse includes obtaining a first set of network traffic messages of network traffic currently received by a network service and determining, via a first model, whether network abuse is occurring based on the first set of network traffic messages. When the network abuse is occurring, the method includes obtaining a second set of current network traffic messages. The method also includes, for each network traffic message in the second set of network traffic messages, labeling, via a second model, the network traffic message as an abusing network traffic message or a non-abusing network traffic message. The method also includes generating, via a third model, at least one network traffic rule. Each network traffic rule, when implemented, reduces an effect of the abusing network traffic messages.

Abstract: A method for mitigating network abuse includes obtaining a first set of network traffic messages of network traffic currently received by a network service and determining, via a first model, whether network abuse is occurring based on the first set of network traffic messages. When the network abuse is occurring, the method includes obtaining a second set of current network traffic messages. The method also includes, for each network traffic message in the second set of network traffic messages, labeling, via a second model, the network traffic message as an abusing network traffic message or a non-abusing network traffic message. The method also includes generating, via a third model, at least one network traffic rule. Each network traffic rule, when implemented, reduces an effect of the abusing network traffic messages.

Abstract: A technique includes receiving data representing an output of a security scan of an application and an audit of the security scan by a human auditor. The output represents a security issue with the application, which is identified by the security scan, and the audit represents an analysis of the security issue by the human auditor. The technique includes training a security scan classifier to learn a classification preference of the human auditor. Training the security scan classifier includes, processing the data in a processor-based machine to, based at least in part on the output of the security scan and the analysis of the security scan by the human auditor, learn the classification preference of the human auditor to the issue to build a classification model for the issue.

Abstract: A technique includes receiving data representing an output of a security scan of an application and an audit of the security scan by a human auditor. The output represents a security issue with the application, which is identified by the security scan, and the audit represents an analysis of the security issue by the human auditor. The technique includes training a security scan classifier to learn a classification preference of the human auditor. Training the security scan classifier includes, processing the data in a processor-based machine to, based at least in part on the output of the security scan and the analysis of the security scan by the human auditor, learn the classification preference of the human auditor to the issue to build a classification model for the issue.

Abstract: A technique includes receiving issue data, which represents an issue identified by a security scan of an application and attributes of the issue. The technique includes applying a machine classifier to the issue data to prioritize the issue; based at least in part on a human audit of the classified data, generating additional issue data representing a priority correction for the issue; and retraining the classifier based on the additional issue data.

Abstract: A technique includes receiving data representing issues identified in a security scan of an application and features associated with the issues. The technique includes processing the data in a processor-based machine to selectively assign classifiers to the security issues based at least in part on the features. The technique includes using the assigned classifiers to classify the issues.