Predictive scheduled anti-virus scanning
Implementations are provided herein for systems, methods, and a non-transitory computer product configured to use predictive analysis of quantifiable parameters associated with individual files stored on a distributed file storage system. In some embodiments, parameters are analyzed by machine learning so that scheduled antivirus scanning can be intelligently conducted. We teach creating a sequential order for scheduled antivirus scanning such that the files most likely to be accessed or needed by users in the future will be scanned for viruses before those files less likely to be accessed. Our teachings encompass the use of heuristic data compiled and analyzed on a per-file basis. We enable system administrators to determine which parameters to prioritize and to set thresholds for antivirus scanning such as time limits.
1. A method comprising:
evaluating at a node of a distributed file system a plurality of parameters, each parameter being associated with a respective plurality of files stored in the distributed file system, the distributed file system further comprising an ordered scan queue and a predictive scan queue, the predictive scan queue containing one or more files having a statistically significant likelihood of being accessed based on an association between the one or more files and a previous access request, the predictive scan queue being created using a machine learning module trained to analyze the plurality of parameters and the association between the one or more files and the previous access request;
creating a prioritized list of the plurality of stored files based on the plurality of parameters, wherein the plurality of stored files are ordered sequentially according to a size of at least one parameter;
storing the prioritized list of the plurality of stored files in the ordered scan queue, the ordered scan queue comprising the plurality of stored files to undergo a scheduled anti-virus scan on a lower priority than the one or more files in the predictive scan queue; and
performing the scheduled anti-virus scan on the one or more files in the predictive scan queue, followed by the scheduled anti-virus scan on the plurality of stored files in the ordered scan queue according to the sequential order of the prioritized list.
2. The method according to claim 1 , wherein the plurality of parameters are stored in a heuristics data set.
3. The method of claim 1 , wherein the machine learning module analyzes the plurality of parameters in order to create the predictive scan queue and create an order of the prioritized list.
4. The method according to claim 1 , wherein the parameter is one or more of: an access frequency for one of the stored files, a date of a last access request for one of the stored files, a time of a last access request for one of the stored files, a file extension for one of the stored files, a true type for one of the stored files, a hash value for one of the stored files, a reputation for one of the stored files, a publisher for one of the stored files, a size for one of the stored files, an access zone for one of the stored files, a base directory for one of the stored files, a permission bit for one of the stored files, a creation time for one of the stored files, a last modification time for one of the stored files, or a last definition update time for one of the stored files.
5. The method according to claim 1 , further comprising stopping the scheduled anti-virus scan once a threshold condition has been met.
6. The method according to claim 5 , wherein the threshold is one or more of the following: a total runtime amount, a total number of files to scan, a total percentage of files to scan, a total amount of resource consumption, a maximum real-time file access value, or a maximum real-time file usage value.
7. A system comprising at least one storage device and at least one hardware processor configured to:
evaluate at a node of a distributed file system a plurality of parameters, each parameter being associated with a respective plurality of files stored in the distributed file system, the distributed file system further comprising an ordered scan queue and a predictive scan queue, the predictive scan queue containing one or more files having a statistically significant likelihood of being accessed based on an association between the one or more files and a previous access request, and the predictive scan queue being created using a machine learning module trained to analyze the plurality of parameters and the association between the one or more files and the previous access request;
create a prioritized list of the plurality of stored files based on the plurality of parameters, wherein the plurality of stored files are ordered sequentially according to a size of at least one parameter;
store the prioritized list of the plurality of stored files in the ordered scan queue, the ordered scan queue comprising the plurality of stored files to undergo a scheduled anti-virus scan on a lower priority than the one or more files in the predictive scan queue; and
perform the scheduled anti-virus scan on the one or more files in the predictive scan queue, followed by the scheduled anti-virus scan on the plurality of stored files in the ordered scan queue according to the sequential order of the prioritized list.
8. The system of claim 7 , wherein the plurality of parameters are stored in a heuristics data set.
9. The system of claim 7 , wherein the machine learning module analyzes the plurality of parameters in order to create an order of the prioritized list.
10. The system of claim 7 , wherein the parameter is one or more of: an access frequency for one of the stored files, a date of a last access request for one of the stored files, a time of a last access request for one of the stored files, a file extension for one of the stored files, a true type for one of the stored files, a hash value for one of the stored files, a reputation for one of the stored files, a publisher for one of the stored files, a size for one of the stored files, an access zone for one of the stored files, a base directory for one of the stored files, a permission bit for one of the stored files, a creation time for one of the stored files, a last modification time for one of the stored files, or a last definition update time for one of the stored files.
11. The system of claim 7 , further configured to stop the scheduled anti-virus scan once a threshold condition has been met.
12. A non-transitory computer readable medium with program instructions stored thereon the program instructions when executed by a processor cause the processor to perform the following acts:
evaluate at a node of a distributed file system a plurality of parameters, each parameter being associated with a respective plurality of files stored in the distributed file system, the distributed file system further comprising an ordered scan queue and a predictive scan queue, the predictive scan queue containing one or more files having a statistically significant likelihood of being accessed based on an association between the one or more files and a previous access request, the predictive scan queue being created using a machine learning module trained to analyze the plurality of parameters and the association between the one or more files and the previous access request;
create a prioritized list of the plurality of stored files based on the plurality of parameters, wherein the plurality of stored files are ordered sequentially according to a size of at least one parameter;
store the prioritized list of the plurality of stored files in the ordered scan queue, the ordered scan queue comprising the plurality of stored files to undergo a scheduled anti-virus scan on a lower priority than the one or more files in the predictive scan queue; and
perform the scheduled anti-virus scan on the one or more files in the predictive scan queue, followed by the scheduled anti-virus scan on the plurality of stored files in the ordered scan queue according to the sequential order of the prioritized list.
13. The non-transitory computer readable medium of claim 12 , wherein the plurality of parameters are stored in a heuristics data set.
14. The non-transitory computer readable medium of claim 12 , wherein the machine learning module analyzes the plurality of parameters in order to create the predictive scan queue and create an order of the prioritized list.
15. The non-transitory computer readable medium of claim 12 , wherein the parameter is one or more of: an access frequency for one of the stored files, a date of a last access request for one of the stored files, a time of a last access request for one of the stored files, a file extension for one of the stored files, a true type for one of the stored files, a hash value for one of the stored files, a reputation for one of the stored files, a publisher for one of the stored files, a size for one of the stored files, an access zone for one of the stored files, a base directory for one of the stored files, a permission bit for one of the stored files, a creation time for one of the stored files, a last modification time for one of the stored files, or a last definition update time for one of the stored files.
16. The non-transitory computer readable medium of claim 12 with program instructions stored thereon to further perform the following acts: stop the scheduled anti-virus scan once a threshold condition has been met.
17. The non-transitory computer readable medium of claim 16 , wherein the threshold is one or more of the following: a total runtime amount, a total number of files to scan, a total percentage of files to scan, a total amount of resource consumption, a maximum real-time file access value, or a maximum real-time file usage value.