Abstract: Metric data may be received, where the metric data may include one or more time series, where each time series may include a set of metric datapoints corresponding to a set of time slices, and where each metric datapoint may be a measurement of a metric of a monitored object in an infrastructure. An infrastructure health score may be computed for each time slice in the set of time slices based on the metric data. A machine learning (ML) model may be trained based on metric datapoints corresponding to at least a current time slice and the infrastructure health score corresponding to a future time slice. The trained ML model may then be used to predict a future infrastructure health score based on at least current metric datapoints.

Abstract: Systems and techniques are described to facilitate using secure tokens for stateless software defined networking. An initial configuration may be created for deploying a network device at a deployment site. A cryptographically secure certificate may be created that includes the initial configuration for deploying the network device at the deployment site. The cryptographically secure certificate may be stored in a secure token that can be inserted into a secure token reader that is located at the deployment site and communicatively coupled to the device at the deployment site. The network device may then be configured at the deployment site by using the secure token.

Abstract: The disclosed embodiments provide a system that modifies execution of a target process in a computer system. During loading of a marker library by a target process, the system modifies import dependency data of the marker library to include an injection library as a dependency of a marker library. After the modified import dependency data is used to load the injection library into the target process by the operating system or loader, the system executes the injection library prior to execution of the marker library to revert the import dependency data to an original state and modify execution of the target process.

Abstract: Systems and techniques are described to detect and recover from ransomware infections. It may be determined if a ransomware attack is in progress based on analyzing read and write requests to a file system. Next, a mitigation action may be performed in response to determining that a ransomware attack is in progress.