Abstract: An endpoint in an enterprise network is monitored, and when a potential trigger for a distributed denial of service (DDoS) attack is followed by an increase in network traffic from the endpoint to a high reputation network address, the endpoint is treated as a DDoS service bot and isolated from the network until remediation can be performed.

Abstract: A data recorder stores endpoint activity on an ongoing basis as sequences of events that causally relate computer objects such as processes and files. When a security event is detected, an event graph may be generated based on these causal relationships among the computing objects. For a root cause analysis, the event graph may be traversed in a reverse order from the point of an identified security event (e.g., a malware detection event) to preceding computing objects, while applying one or more cause identification rules to identify a root cause of the security event. Once a root cause is identified, the event graph may be traversed forward from the root cause to identify other computing objects that are potentially compromised by the root cause.

Abstract: A threat management facility detects a device on an enterprise network and determines whether the device is one of a set of managed devices for the enterprise network. When the device is not one of the set of managed devices, the device may be directed to a portal that manages admission of unrecognized devices onto the enterprise network. Based on a response of the unrecognized device to the portal (e.g., if the unrecognized device does not respond to the portal), the device may be listed on an unclaimed device page published by the portal and accessible to authorized users of the enterprise network. An authorized user may claim the unrecognized device from the unclaimed device page and, in the process, may provide additional information regarding the unrecognized device. Once claimed, the previously unrecognized device may be permitted to communicate over the enterprise network.

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: Entity models are used to evaluate potential risk of entities, either individually or in groups, in order to evaluate suspiciousness within an enterprise network. These individual or aggregated risk assessments can be used to adjust the security policy for compute instances within the enterprise network. A security policy may specify security settings such as network speed, filtering levels, network isolation, levels of privilege, and the like.

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: An authentication model dynamically adjusts authentication factors required for access to a remote resource based on changes to a risk score for a user, a device, or some combination of these. For example, the authentication model may conditionally specify the number and type of authentication factors required by a user/device pair, and may dynamically alter authentication requirements based on changes to a current risk assessment for the user/device while the remote resource is in use.

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: A threat management facility detects a device on an enterprise network and determines whether the device is one of a set of managed devices for the enterprise network. When the device is not one of the set of managed devices, the threat management facility may determine whether the device is manageable. When the device is unrecognized and unmanageable, a portal may provide support to a user of the device by listing the device on an unclaimed device page published by the portal and accessible to authorized users of the enterprise network. An authorized user may claim the unrecognized device from the unclaimed device page and, in the process, may provide additional information regarding the unrecognized device. Once claimed, the previously unrecognized device may be permitted to communicate over the enterprise network.

Abstract: A threat management facility detects a device on an enterprise network and determines whether the device is one of a set of managed devices for the enterprise network. When the device is not one of the set of managed devices, the threat management facility may determine whether the device is manageable. When the device is unrecognized and unmanageable, a portal may provide support to a user of the device by listing the device on an unclaimed device page published by the portal and accessible to authorized users of the enterprise network. An authorized user may claim the unrecognized device from the unclaimed device page and, in the process, may provide additional information regarding the unrecognized device. Once claimed, the previously unrecognized device may be permitted to communicate over the enterprise network.

Abstract: A file system extension for an endpoint controls access to files by selectively decrypting files under certain conditions. Where a pattern of access to the files suggests malicious and/or automated file access activity, the file system extension may limit the rate of file access by regulating the rate at which decryption is provided to requesting processes.

Abstract: Secure management of an enterprise network is improved by creating a network adapter fingerprint for an endpoint that identifies all of the network adapters for that endpoint. With this information, the location and connectivity of the endpoint can be tracked and managed independent of the manner in which the endpoint is connecting to the enterprise network.

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: 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: Threat detection instrumentation is simplified by providing and updating labels for computing objects in a context-sensitive manner. This may include simple labeling schemes to distinguish between objects, e.g., trusted/untrusted processes or corporate/private data. This may also include more granular labeling schemes such as a three-tiered scheme that identifies a category (e.g., financial, e-mail, game), static threat detection attributes (e.g., signatures, hashes, API calls), and explicit identification (e.g., what a file or process calls itself). By tracking such data for various computing objects and correlating these labels to malware occurrences, rules can be written for distribution to endpoints to facilitate threat detection based on, e.g., interactions of labeled objects, changes to object labels, and so forth.

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 enterprise security system is improved by instrumenting endpoints to explicitly label network flows with cryptographically secure labels that identify an application or other source of each network flow. Cryptographic techniques may be used, for example, to protect the encoded information in the label from interception by third parties or to support cryptographic authentication of a source of each label. A label may provide health, status, or other heartbeat information for the endpoint, and may be used to identify compromised endpoints, to make routing decisions for network traffic (e.g., allowing, blocking, rerouting, etc.), to more generally evaluate the health of an endpoint that is sourcing network traffic, or for any other useful purpose.

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 enterprise security system is improved by instrumenting endpoints to explicitly label network flows with cryptographically secure labels that identify an application or other source of each network flow. Cryptographic techniques may be used, for example, to protect the encoded information in the label from interception by third parties or to support cryptographic authentication of a source of each label. A label may provide health, status, or other heartbeat information for the endpoint, and may be used to identify compromised endpoints, to make routing decisions for network traffic (e.g., allowing, blocking, rerouting, etc.), to more generally evaluate the health of an endpoint that is sourcing network traffic, or for any other useful purpose.

Abstract: Network devices within an enterprise are configured to pass out-of-band security information such as heartbeats, notifications of compromise, device identification information, and so forth between logical or physical network partitions such as subnets, routing domains, access points, and so forth. This technique can advantageously facilitate integrated management of endpoints across network boundaries that might otherwise interfere with the identification and management of specific devices.