Abstract: Where a single networked security service supports multiple enterprises, this security service can operate as a shared source of trust so that security devices associated with one enterprise can provide authenticated, policy-based management of computing devices associated with another enterprise. For example, an enterprise firewall can advantageously manage network access for a new device based on a shared and authenticated relationship with the networked security service.

Abstract: An endpoint in an enterprise network is instrumented with sensors to detect security-related events occurring on the endpoint. Event data from these sensors is augmented with contextual information about, e.g., a source of each event in order to facilitate improved correlation, analysis, and visualization at a threat management facility for the enterprise network.

Abstract: A threat management system provides a collection of queries for investigating security issues within an enterprise. Useful inferences are drawn about the value of different queries, and about the security posture of the enterprise, by monitoring contextual activity such as the popularity and context of query usage, patterns of end user modification to queries, and post-query activity.

Abstract: Trampoline and return-oriented programming attacks employ a variety of techniques to maliciously execute instructions on a device in a manner different from a legitimate programmer's original intent. By instrumenting a device to detect deviations from predicted behavior, these exploits can be identified and mitigated.

Abstract: Attempts at lateral movement are detected by monitoring failed login attempts across a number of endpoints in a network. By configuring endpoints across the network to report unsuccessful login attempts and monitoring these login attempts at a central location, patterns of attempts and failures may advantageously be detected and used to identify malicious attempts at lateral movement within the network before any unauthorized lateral movement is achieved.

Abstract: An endpoint in a network periodically generates a heartbeat encoding health state information and transmits this heartbeat to other network entities. Recipients of the heartbeat may use the health state information to independently make decisions about communications with the source endpoint, for example, by isolating the endpoint to prevent further communications with other devices sharing the network with the endpoint. Isolation may be coordinated by a firewall or gateway for the network, or independently by other endpoints that receive a notification of the compromised health state.

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: 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: In the context of network activity by an endpoint in an enterprise network, malware detection is improved by using a combination of reputation information for a network address that is accessed by the endpoint with reputation information for an application on the endpoint that is accessing the network address. This information, when combined with a network usage history for the application, provides improved differentiation between malicious network activity and legitimate, user-initiated network activity.

Abstract: In a virtualized environment where multiple guest virtual machines receive security services from multiple security virtual machines, a guest virtual machine automatically transitions to a new virtual security machine under various conditions. For example, the guest virtual machine may select a new security virtual machine when connectivity to the current security virtual machine degrades below a predetermined threshold, or in response to a request from the current security virtual machine indicating, e.g., that the current security virtual machine is about to shut down or otherwise terminate security services to the guest virtual machine.

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 endpoint in an enterprise network is configured to respond to internal and external detections of compromise in a manner that permits the endpoint to cooperate with other endpoints to secure the enterprise network. For example, the endpoint may be configured to self-isolate when local monitoring detects a compromise on the endpoint, and to respond to an external notification of compromise of another endpoint by restricting communications with that other endpoint.

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: 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: 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 collection of documents or other files and the like within an enterprise network are labelled according to an enterprise document classification scheme, and then a recognition model such as a neural network or other machine learning model can be used to automatically label other files throughout the enterprise network. In this manner, documents and the like throughout an enterprise can be automatically identified and managed according to features such as confidentiality, sensitivity, security risk, business value, and so forth.