Abstract: In a threat management platform, a number of endpoints log events in an event data recorder. A local agent filters this data and feeds a filtered data stream to a central threat management facility. The central threat management facility can locally or globally tune filtering by local agents based on the current data stream, and can query local event data recorders for additional information where necessary or helpful in threat detection or forensic analysis. The central threat management facility also stores and deploys a number of security tools such as a web-based user interface supported by machine learning models to identify potential threats requiring human intervention and other models to provide human-readable context for evaluating potential threats.

Abstract: A computer model is created for automatically evaluating the business value of computing objects such as files and databases on an endpoint. This can be used to assess the potential business impact of a security compromise to an endpoint, or a process executing on an endpoint, in order to prioritize potential threats within an enterprise for human review and intervention.

Abstract: An automated system attempts to characterize code as safe or unsafe. For intermediate code samples not placed with sufficient confidence in either category, human-readable analysis is automatically generated to assist a human reviewer in reaching a final disposition. For example, a random forest over human-interpretable features may be created and used to identify suspicious features in a manner that is understandable to, and actionable by, a human reviewer. Similarly, a k-nearest neighbor algorithm may be used to identify similar samples of known safe and unsafe code based on a model for, e.g., a file path, a URL, an executable, and so forth. Similar code may then be displayed (with other information) to a user for evaluation in a user interface. This comparative information can improve the speed and accuracy of human interventions by providing richer context for human review of potential threats.

Abstract: An endpoint coupled in a communicating relationship with an enterprise network may include a data recorder configured to store an event stream of data indicating events on the endpoint including types of changes to computing objects, a filter configured to locally process the event stream into a filtered event stream including a subset of types of changes to the computing objects, and a local security agent. The local security agent may be configured to transmit the filtered event stream to a threat management facility, respond to a filter adjustment from the threat management facility by adjusting the filter to modify the subset of types of changes included in the filtered event stream, and respond to a query from the threat management facility by retrieving data stored in the data recorder over a time window before the query and excluded from the filtered event stream.

Abstract: In a threat management platform, a number of endpoints log events in an event data recorder. A local agent filters this data and feeds a filtered data stream to a central threat management facility. The central threat management facility can locally or globally tune filtering by local agents based on the current data stream, and can query local event data recorders for additional information where necessary or helpful in threat detection or forensic analysis. The central threat management facility also stores and deploys a number of security tools such as a web-based user interface supported by machine learning models to identify potential threats requiring human intervention and other models to provide human-readable context for evaluating potential threats.

Abstract: An automated system attempts to characterize code as safe or unsafe. For intermediate code samples not placed with sufficient confidence in either category, human-readable analysis is automatically generated to assist a human reviewer in reaching a final disposition. For example, a random forest over human-interpretable features may be created and used to identify suspicious features in a manner that is understandable to, and actionable by, a human reviewer. Similarly, a k-nearest neighbor algorithm may be used to identify similar samples of known safe and unsafe code based on a model for, e.g., a file path, a URL, an executable, and so forth. Similar code may then be displayed (with other information) to a user for evaluation in a user interface. This comparative information can improve the speed and accuracy of human interventions by providing richer context for human review of potential threats.

Abstract: Attachments or other documents can be transmitted to a sandbox environment where they can be concurrently opened for remote preview from an endpoint and scanned for possible malware. A gateway or other intermediate network element may enforce this process by replacing attachments, for example, in incoming electronic mail communications, with links to a document preview hosted in the sandbox environment.

Abstract: An endpoint coupled in a communicating relationship with an enterprise network may include a data recorder configured to store an event stream of data indicating events on the endpoint including types of changes to computing objects, a filter configured to locally process the event stream into a filtered event stream including a subset of types of changes to the computing objects, and a local security agent. The local security agent may be configured to transmit the filtered event stream to a threat management facility, respond to a filter adjustment from the threat management facility by adjusting the filter to modify the subset of types of changes included in the filtered event stream, and respond to a query from the threat management facility by retrieving data stored in the data recorder over a time window before the query and excluded from the filtered event stream.

Abstract: In a threat management platform, a number of endpoints log events in an event data recorder. A local agent filters this data and feeds a filtered data stream to a central threat management facility. The central threat management facility can locally or globally tune filtering by local agents based on the current data stream, and can query local event data recorders for additional information where necessary or helpful in threat detection or forensic analysis. The central threat management facility also stores and deploys a number of security tools such as a web-based user interface supported by machine learning models to identify potential threats requiring human intervention and other models to provide human-readable context for evaluating potential threats.

Abstract: A honeypot file is cryptographically secured with a cryptographic key. The key, or related key material, is then placed on a central keystore and the file is placed on a data store within the enterprise network. Unauthorized access to the honeypot file can then be detecting by monitoring use of the associated key material, which usefully facilitates detection of file access at any time when, and from any location where, cryptographic access to the file is initiated.

Abstract: A multi-endpoint event graph causally relates a sequence of events among a number of computing objects at a number of logical locations including multiple endpoints in an enterprise network. The multi-endpoint event graph is used to detect malware based on malicious software moving through the enterprise network.

Abstract: An ensemble of detection techniques are used to identify code that presents intermediate levels of threat. For example, an ensemble of machine learning techniques may be used to evaluate suspiciousness based on binaries, file paths, behaviors, reputations, and so forth, and code may be sorted into safe, unsafe, intermediate, or any similar categories. By filtering and prioritizing intermediate threats with these tools, human threat intervention can advantageously be directed toward code samples and associated contexts most appropriate for non-automated responses.

Abstract: A multi-endpoint event graph is used to detect malware based on malicious software moving through a network.

Abstract: An endpoint coupled in a communicating relationship with an enterprise network may include a data recorder configured to store an event stream of data indicating events on the endpoint including types of changes to computing objects, a filter configured to locally process the event stream into a filtered event stream including a subset of types of changes to the computing objects, and a local security agent. The local security agent may be configured to transmit the filtered event stream to a threat management facility, respond to a filter adjustment from the threat management facility by adjusting the filter to modify the subset of types of changes included in the filtered event stream, and respond to a query from the threat management facility by retrieving data stored in the data recorder over a time window before the query and excluded from the filtered event stream.

Abstract: A technique for dynamically updating a user interface for threat investigation may include receiving a scheduled transmittal of events in an event stream from an endpoint at a threat management facility, processing the event stream at the threat management facility to detect an intermediate threat, in response to detecting the intermediate threat at the threat management facility, requesting a transmittal of supplemental information from a data recorder on the endpoint, receiving the supplemental information in a supplemental transmittal from the endpoint to the threat management facility, and displaying a description of the intermediate threat and the supplemental information in a user interface hosted by the threat management facility, where the user interface is configured for user investigation and disposition of the intermediate threat.

Abstract: An event graph can be generated, and, upon malware detection, traversed backward to identify a root cause associated with the malware detection. Using this information, rules for earlier malware detection can be created by analyzing the event graph proximal to the root cause rather than proximal to the malware detection trigger.

Abstract: An event graph associated with a root cause for a change in security state on an endpoint is used to facilitate malware detection on other endpoints.

Abstract: A ledger stores chain of custody information for files throughout an enterprise network. By identifying files with a homologous identifier such as a fuzzy hash that permits piecewise evaluation of similarity, the ledger can be used to track a chain of custody over a sequence of changes in content, ownership, and file properties. The ledger can be used, e.g., to evaluate trustworthiness of a file the first time it is encountered by an endpoint, or to apply enterprise policies based on trust.

Abstract: Activity on an endpoint is monitored in two stages with a local agent. In a first stage, particular computing objects on the endpoint are selected for tracking. In a second stage, particular types of changes to those objects are selected. By selecting objects and object changes in this manner, a compact data stream of information highly relevant to threat detection can be provided from an endpoint to a central threat management facility. At the same time, a local data recorder creates a local record of a wider range of objects and changes. The system may support forensic activity by facilitating queries to the local data recorder on the endpoint to retrieve more complete records of local activity when the compact data stream does not adequately characterize a particular context.

Abstract: A ledger stores chain of custody information for files throughout an enterprise network. By identifying files with a homologous identifier such as a fuzzy hash that permits piecewise evaluation of similarity, the ledger can be used to track a chain of custody over a sequence of changes in content, ownership, and file properties. The ledger can be used, e.g., to evaluate trustworthiness of a file the first time it is encountered by an endpoint, or to apply enterprise policies based on trust.