Abstract: 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.

Abstract: Implementations are provided herein for enhanced antivirus scanning in distributed file storage systems. In embodiments, we disclose using predictive, heuristic data to determine prospectively a sequence of files that may be called by a client based upon relationships between an initial filed called by a client and other files stored in the distributed file system. The sequence of files likely to be called in the future could, in some embodiments, be determined using machine language trained to analyze past client behavior patterns. Once a predictive file sequence has been determined, embodiments herein send files likely to be called in the very near future to an antivirus server for processing during a time period that overlaps the AV processing period for the original file that was called.

Abstract: 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 real-time and 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. We reconcile scheduled antivirus scanning with predictive analysis for real-time antivirus scanning. Our teachings encompass the use of heuristic data compiled, in some instances through machine learning, 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.

Abstract: 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.

Abstract: Implementations are provided herein for enhanced antivirus scanning in distributed file storage systems. In embodiments, we disclose using predictive, heuristic data to determine prospectively a sequence of files that may be called by a client based upon relationships between an initial filed called by a client and other files stored in the distributed file system. The sequence of files likely to be called in the future could, in some embodiments, be determined using machine language trained to analyze past client behavior patterns. Once a predictive file sequence has been determined, embodiments herein send files likely to be called in the very near future to an antivirus server for processing during a time period that overlaps the AV processing period for the original file that was called.

Abstract: 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 real-time and 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. We reconcile scheduled antivirus scanning with predictive analysis for real-time antivirus scanning. Our teachings encompass the use of heuristic data compiled, in some instances through machine learning, 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.