Using indications of compromise for reputation based network security
View Patent ↗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. In this manner, threat detection based on complex interactions of computing objects can be characterized in a platform independent manner and pre-processed on endpoints without requiring significant communications overhead with a remote threat management facility.
1. A method comprising:
collecting a plurality of indications of compromise from an endpoint, each one of the indications of compromise based upon one or more actions taken by one or more processes executing on the endpoint, and at least one of the plurality of indications of compromise including a category of one or more objects related to the one or more actions and a description of one or more objects related to the one or more actions;
identifying a low-reputation behavior based upon a quantity of other endpoints that have seen the plurality of indications of compromise and a context for the one or more actions on the endpoint;
creating a coloring rule for the low-reputation behavior based upon an occurrence of the plurality of indications of compromise;
receiving the coloring rule at a security facility on the endpoint; and
applying the coloring rule with the security facility to color low reputation objects.
2. The method of claim 1 , wherein the context for the one or more actions on the endpoint includes an inferred behavior of the one or more objects.
3. The method of claim 1 , wherein the context for the one or more actions includes a source of the one or more objects.
4. The method of claim 1 , wherein the context for the one or more actions includes a type of the one or more objects.
5. The method of claim 1 , wherein the at least one of the plurality of indications of compromise further includes a specific identification of one of the objects.
6. The method of claim 1 , wherein the at least one of the plurality of indications of compromise further includes a genetic identification of one of the one or more objects based on one or more characteristics or actions of the one of the one or more objects.
7. The method of claim 1 , wherein the category includes an application type.
8. A method comprising:
collecting a plurality of indications of compromise from an endpoint, each indication of compromise based upon one or more actions taken by one or more processes executing on the endpoint, and at least one of the plurality of indications of compromise including a category of one or more objects related to the one or more actions and a description of one or more objects related to the one or more actions;
identifying a low-reputation behavior based upon a quantity of other endpoints that have seen the plurality of indications of compromise and a context for the one or more actions on the endpoint;
creating a coloring rule for the low-reputation behavior based upon an occurrence of the plurality of indications of compromise; and
when the plurality of indications of compromise are detected on the endpoint, taking an action based upon the coloring rule.
9. The method of claim 8 , wherein taking an action includes initiating a remedial action for the endpoint.
10. The method of claim 8 , wherein the context for the one or more actions on the endpoint includes an inferred behavior of the one or more objects.
11. The method of claim 8 , wherein the context for the one or more actions includes a source of the one or more objects.
12. The method of claim 8 , wherein the context for the one or more actions includes a type of the one or more objects.
13. The method of claim 8 , wherein the at least one of the plurality of indications of compromise further includes a specific identification of one of the objects.
14. A computer program product comprising a non-transitory computer readable medium having stored thereon computer executable code that, when executing on one or more computing devices, cause the one or more computing devices to perform the steps of:
collecting a plurality of indications of compromise from an endpoint, each one of the indications of compromise based upon one or more actions taken by one or more processes executing on the endpoint, and at least one of the plurality of indications of compromise including a category of one or more objects related to the one or more actions and a description of the one or more objects related to the one or more actions;
identifying a low-reputation behavior based upon a quantity of other endpoints that have seen the plurality of indications of compromise and a context for the one or more actions on the endpoint;
creating a coloring rule for the low-reputation behavior based upon an occurrence of the plurality of indications of compromise;
receiving the coloring rule at a security facility on the endpoint; and
applying the coloring rule with the security facility to color low reputation objects.
15. The computer program product of claim 14 , wherein the computer executable code further causes the one or more computing devices to perform the step of applying the coloring rule to identify the low-reputation behavior based on the occurrence of the plurality of indications of compromise.
16. The computer program product of claim 14 , wherein the at least one of the plurality of indications of compromise includes a specific identification of one of the objects.
17. The computer program product of claim 14 , wherein the at least one of the plurality of indications of compromise includes a genetic identification of one of the objects based on one or more characteristics or actions of the one of the objects.
18. The computer program product of claim 14 , wherein the context for the one or more actions on the endpoint includes an inferred behavior of the one or more objects.
19. The computer program product of claim 14 , wherein the context for the one or more actions includes a source of the one or more objects.
20. The computer program product of claim 14 , wherein the context for the one or more actions includes a source of the one or more objects.