Abstract: Techniques for automatically grouping malware based on artifacts are disclosed. In some embodiments, a system, process, and/or computer program product for automatically grouping malware based on artifacts includes receiving a plurality of samples for performing automated malware analysis to generate log files based on the automated malware analysis; processing the log files to extract features associated with malware; clustering the plurality of samples based on the extracted features; and performing an action based on the clustering output.

Abstract: The disclosed embodiments provide a system for managing a data store. During operation, the system stores a set of pending updates to a data store in a registry. Next, the system executes an asynchronous process that applies a first subset of updates from the registry as writes to records in the data store without blocking processing of read queries of the data store. Upon completing a write by the asynchronous process at a second portion of the data store, the system updates the registry with an indication of the completed write at the second portion of the data store. During processing of a read query of the data store, the system applies a second subset of updates from the registry to a result of the read query. Finally, the system returns the result in a response to the read query.

Abstract: Techniques for automatically grouping malware based on artifacts are disclosed. In some embodiments, a system, process, and/or computer program product for automatically grouping malware based on artifacts includes receiving a plurality of samples for performing automated malware analysis to generate log files based on the automated malware analysis; processing the log files to extract features associated with malware; clustering the plurality of samples based on the extracted features; and performing an action based on the clustering output.

Abstract: Techniques for automatically grouping malware based on artifacts are disclosed. In some embodiments, a system, process, and/or computer program product for automatically grouping malware based on artifacts includes receiving a plurality of samples for performing automated malware analysis to generate log files based on the automated malware analysis; processing the log files to extract features associated with malware; clustering the plurality of samples based on the extracted features; and performing an action based on the clustering output.

Abstract: Techniques for alerting and tagging using a malware analysis platform for threat intelligence made actionable are disclosed. In some embodiments, a system, process, and/or computer program product for alerting and tagging using a malware analysis platform for threat intelligence made actionable includes receiving a plurality of samples for performing automated malware analysis to generate log files based on the automated malware analysis; processing the log files to extract artifacts associated with the log files; determining whether a tag matches any of the plurality of samples based on the artifacts; and performing an action based on whether the tag matches any of the plurality of samples.

Abstract: Techniques for alerting and tagging using a malware analysis platform for threat intelligence made actionable are disclosed. In some embodiments, a system, process, and/or computer program product for alerting and tagging using a malware analysis platform for threat intelligence made actionable includes receiving a plurality of samples for performing automated malware analysis to generate log files based on the automated malware analysis; processing the log files to extract artifacts associated with the log files; determining whether a tag matches any of the plurality of samples based on the artifacts; and performing an action based on whether the tag matches any of the plurality of samples.

Abstract: Techniques for alerting and tagging using a malware analysis platform for threat intelligence made actionable are disclosed. In some embodiments, a system, process, and/or computer program product for alerting and tagging using a malware analysis platform for threat intelligence made actionable includes receiving a plurality of samples for performing automated malware analysis to generate log files based on the automated malware analysis; processing the log files to extract artifacts associated with the log files; determining whether a tag matches any of the plurality of samples based on the artifacts; and performing an action based on whether the tag matches any of the plurality of samples.

Abstract: Techniques for alerting and tagging using a malware analysis platform for threat intelligence made actionable are disclosed. In some embodiments, a system, process, and/or computer program product for alerting and tagging using a malware analysis platform for threat intelligence made actionable includes receiving a plurality of samples for performing automated malware analysis to generate log files based on the automated malware analysis; processing the log files to extract artifacts associated with the log files; determining whether a tag matches any of the plurality of samples based on the artifacts; and performing an action based on whether the tag matches any of the plurality of samples.

Abstract: Techniques for automatically grouping malware based on artifacts are disclosed. In some embodiments, a system, process, and/or computer program product for automatically grouping malware based on artifacts includes receiving a plurality of samples for performing automated malware analysis to generate log files based on the automated malware analysis; processing the log files to extract features associated with malware; clustering the plurality of samples based on the extracted features; and performing an action based on the clustering output.

Abstract: Techniques for automatically grouping malware based on artifacts are disclosed. In some embodiments, a system, process, and/or computer program product for automatically grouping malware based on artifacts includes receiving a plurality of samples for performing automated malware analysis to generate log files based on the automated malware analysis; processing the log files to extract features associated with malware; clustering the plurality of samples based on the extracted features; and performing an action based on the clustering output.

Abstract: Techniques for automatically determining whether malware samples are similar are disclosed. In some embodiments, a system, process, and/or computer program product for automatically determining whether malware samples are similar includes receiving a plurality of samples for performing automated malware analysis to generate log files based on the automated malware analysis; comparing the log files based on the automated malware analysis; determining whether any of the plurality of samples are similar based on the comparison of the log files based on the automated malware analysis; and performing an action based on determining that at least two samples are similar.

Abstract: Techniques for a malware analysis platform for threat intelligence made actionable are disclosed. In some embodiments, a system, process, and/or computer program product for a malware analysis platform for threat intelligence made actionable includes receiving a plurality of samples for performing automated malware analysis to generate log files based on the automated malware analysis; processing the log files to determine artifacts associated with malware; and performing an action based on an artifact.

Abstract: Techniques for automatically determining whether malware samples are similar are disclosed. In some embodiments, a system, process, and/or computer program product for automatically determining whether malware samples are similar includes receiving a plurality of samples for performing automated malware analysis to generate log files based on the automated malware analysis; comparing the log files based on the automated malware analysis; determining whether any of the plurality of samples are similar based on the comparison of the log files based on the automated malware analysis; and performing an action based on determining that at least two samples are similar.

Abstract: Techniques for a malware analysis platform for threat intelligence made actionable are disclosed. In some embodiments, a system, process, and/or computer program product for a malware analysis platform for threat intelligence made actionable includes receiving a plurality of samples for performing automated malware analysis to generate log files based on the automated malware analysis; processing the log files to determine artifacts associated with malware; and performing an action based on an artifact.

Abstract: Techniques for alerting and tagging using a malware analysis platform for threat intelligence made actionable are disclosed. In some embodiments, a system, process, and/or computer program product for alerting and tagging using a malware analysis platform for threat intelligence made actionable includes receiving a plurality of samples for performing automated malware analysis to generate log files based on the automated malware analysis; processing the log files to extract artifacts associated with the log files; determining whether a tag matches any of the plurality of samples based on the artifacts; and performing an action based on whether the tag matches any of the plurality of samples.

Abstract: A power aware Point-Of-Presence design and auto-configuration method is disclosed for configuring which network elements within a point-of-presence are powered so as to accommodate anticipated traffic while minimizing power consumption. The IP power aware Point-Of-Presence design and auto-configuration method includes a set of activation thresholds associated with each port in the Point-of-Presence that indicates that the port should be activated when the traffic demand exceeds the threshold. The power aware Point-Of-Presence design and auto-configuration method is particularly useful for providing a configuration which accounts for both external and internal link traffic demand and has the further advantage of ordering the port activation such that only a minimum number of activation and deactivation operations to satisfy a new demand is required. This property is advantageous for limiting the routing instabilities caused by topological changes in response to varying demand.

Abstract: A power aware Point-Of-Presence design and auto-configuration method is disclosed for configuring which network elements within a point-of-presence are powered so as to accommodate anticipated traffic while minimizing power consumption. The IP power aware Point-Of-Presence design and auto-configuration method includes a set of activation thresholds associated with each port in the Point-of-Presence that indicates that the port should be activated when the traffic demand exceeds the threshold. The power aware Point-Of-Presence design and auto-configuration method is particularly useful for providing a configuration which accounts for both external and internal link traffic demand and has the further advantage of ordering the port activation such that only a minimum number of activation and deactivation operations to satisfy a new demand is required. This property is advantageous for limiting the routing instabilities caused by topological changes in response to varying demand.