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: 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: 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: 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: 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: 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 gateway or other network device may be configured to monitor endpoint behavior, and to request a verification of user presence at the endpoint under certain conditions suggesting, e.g., malware or other endpoint compromise. For example, when a network request is directed to a low-reputation or unknown network address, user presence may be verified to ensure that this action was initiated by a human user rather than automatically by malware or the like. User verification may be implicit, based on local behavior such as keyboard or mouse activity, or the user verification may be explicit, such as where a notification is presented on a display of the endpoint requesting user confirmation to proceed.

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: Threat detection is improved by monitoring variations in observable events and correlating these variations to malicious activity. The disclosed techniques can be usefully employed with any attribute or other metric that can be instrumented on an endpoint and tracked over time including observable events such as changes to files, data, software configurations, operating systems, and so forth. Correlations may be based on historical data for a particular machine, or a group of machines such as similarly configured endpoints. Similar inferences of malicious activity can be based on the nature of a variation, including specific patterns of variation known to be associated with malware and any other unexpected patterns that deviate from normal behavior. Embodiments described herein use variations in, e.g., server software updates or URL cache hits on an endpoint, but the techniques are more generally applicable to any endpoint attribute that varies in a manner correlated with malicious activity.

Abstract: An anti-cheating system may comprise a combination of a modified environment, such as a modified operating system, in conjunction with a trusted external entity to verify that the modified environment is running on a particular device. The modified environment may be may be modified in a particular manner to create a restricted environment as compared with an original environment which is replaced by the modified environment. The modifications to the modified environment may comprise alternations to the original environment to, for example, detect and/or prevent changes to the hardware and/or software intended to allow cheating or undesirable user behavior.

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: 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: 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: An enterprise security system is improved by instrumenting endpoints to explicitly label network flows according to sources of network traffic. When a network message from an endpoint is received at a gateway, firewall, or other network device/service, the network message may be examined to determine the application on the endpoint that originated the request, and this source information may be used to control routing or other handling of the network message.

Abstract: In one aspect, a method for securing a device includes receiving a first set of boot information of a device, receiving a first cryptographic proof of the first set of boot information, receiving a second set of boot information of the device, receiving a second cryptographic proof of the second set of boot information, comparing the first set of boot information and the second set of boot information, and, upon determining that the first set of boot information and the second set of boot information are different, determining whether differences between the first set of boot information and the second set of boot information are permitted. The method may also include generating an alert upon determining that differences between the first set of boot information and the second set of boot information are not permitted.

Abstract: In one aspect, a method for securing a device includes receiving a first set of boot information from a first device, the first set of boot information including a first list of boot items, receiving from the first device a first proof based on the first set of boot information, verifying the first set of boot information based on the first proof, determining a reputation for one or more of the boot items in the first list of boot items. and reporting the determined reputation.

Abstract: An endpoint encrypts local files with a key to protect file contents. If the endpoint or processes on the endpoint becomes exposed to potentially harmful locations or resources, the key can be revoked to prevent access to encrypted files on the endpoint. In order to facilitate continued operation of the endpoint, files that are currently open can be encrypted with a second key so that the corresponding data is isolated from the other encrypted files while remaining accessible to current users.

Abstract: A portable encryption format wraps encrypted files in a self-executing container that facilitates transparent, identity-based decryption for properly authenticated users while also providing local password access to wrapped files when identity-based decryption is not available.