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 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: 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: 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: 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: 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 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: 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: 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: 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: 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.