Abstract: Electronic communications passing through a communication gateway or similar device for an enterprise can be monitored for indicators of malicious activity. When potentially malicious activity is identified, a user-based inquiry can be employed to identify potential sources of the malicious activity within the enterprise network. More specifically, by identifying a user that sourced the communication, instead of or in addition to a network address, devices within the enterprise network associated with the user can be located, analyzed, and remediated as appropriate.

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.

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 data recorder stores endpoint activity on an ongoing basis as sequences of events that causally relate computer objects such as processes and files, and patterns within this event graph can be used to detect the presence of malware on the endpoint. The underlying recording process may be dynamically adjusted in order to vary the amount and location of recording as the security state of the endpoint changes over time.

Abstract: Methods, systems and computer readable media for rogue access point detection are described.

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: 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 variety of techniques are disclosed for detection of advanced persistent threats and similar malware. In one aspect, the detection of certain network traffic at a gateway is used to trigger a query of an originating endpoint, which can use internal logs to identify a local process that is sourcing the network traffic. In another aspect, an endpoint is configured to periodically generate and transmit a secure heartbeat, so that an interruption of the heartbeat can be used to signal the possible presence of malware. In another aspect, other information such as local and global reputation information is used to provide context for more accurate malware detection.

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: 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: Securing an endpoint against exposure to unsafe content includes encrypting files to prevent unauthorized access, and monitoring an exposure state of a process to potentially unsafe content by applying behavioral rules to determine whether the exposure state is either exposed or secure, where (1) the process is initially identified as secure, (2) the process is identified as exposed when the process opens a network connection to a URL that is not internal to an enterprise network of the endpoint and that has a poor reputation, (3) the process is identified as exposed when it opens a file identified as exposed, and (4) the process is identified as exposed when another exposed process opens a handle to the process. Access to the files may be restricted when the process is exposed by controlling access through a file system filter that conditionally decrypts files for the process according to its exposure state.

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: Securing an endpoint against exposure to unsafe content includes encrypting files to prevent unauthorized access, and monitoring an exposure state of a process to potentially unsafe content by applying behavioral rules to determine whether the exposure state is either exposed or secure, where (1) the process is initially identified as secure, (2) the process is identified as exposed when the process opens a network connection to a URL that is not internal to an enterprise network of the endpoint and that has a poor reputation, (3) the process is identified as exposed when it opens a file identified as exposed, and (4) the process is identified as exposed when another exposed process opens a handle to the process. Access to the files may be restricted when the process is exposed by controlling access through a file system filter that conditionally decrypts files for the process according to its exposure state.

Abstract: Securing an endpoint against malicious activity includes encrypting a plurality of files on an endpoint to prevent unauthorized access to the plurality of files, receiving a request to access a file from a process executing on the endpoint, decrypting the file for the process, and monitoring a security state of the process. If the security state becomes a compromised state, a technique involves maintaining access to any open files (including the file decrypted for the process), prohibiting access to other files, and initiating a remediation of the process by facilitating a restart of the process. If the remediation is successful, access by the process to the plurality of files may be restored.

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: In embodiments of the present invention, a framework for an extensible, file-based security system is described for determining an appropriate application, application environment, and/or access or security control measure based at least in part on a file's reputation. In response to the selection of a file, an application controller may be used to select a software application from two or more software applications to open the selected file, based at least in part on the selected file's reputation. If launched, a software application may be configured to open the file in an environment, such as a virtual machine, quarantined environment, and the like, that is appropriate for the file based at least in part on the reputation information. A software application may be a secure software application configured to manage secure files, or an insecure software application configured to manage insecure files.

Abstract: A variety of techniques are disclosed for detection of advanced persistent threats and similar malware. In one aspect, the detection of certain network traffic at a gateway is used to trigger a query of an originating endpoint, which can use internal logs to identify a local process that is sourcing the network traffic. In another aspect, an endpoint is configured to periodically generate and transmit a secure heartbeat, so that an interruption of the heartbeat can be used to signal the possible presence of malware. In another aspect, other information such as local and global reputation information is used to provide context for more accurate malware detection.

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: 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. In order to support dynamic threat response, the locus and level of detection applied by the local agent can be controlled by the threat management facility and/or the endpoint.

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.