Abstract: A protection controller configured to operate in a computer system, including a process, an operating system and at least one memory disk. The protection controller is further configured to receive a memory disk request for a file, determine that the memory disk request is a WRITE request indicating a modification to the file, and, in response thereto, generate a backup copy of the file prior to the modification. Furthermore, the protection controller is configured to determine that the process includes ransomware and, in response thereto, recover the file based on the backup copy to provide zero-loss ransomware protection to the computer system with an improved overall data security.

Abstract: A master-slave scheduling system, comprising (a) a master DRL unit comprising: (i) a queue containing a plurality of item-representations; (ii) a master policy module configured to select a single item-representation from the queue and submit to the slave unit; (iii) a master DRL agent configured to (a) train the master policy module; and (b) receive an updated item-representation from the slave unit, and update the queue; (b) The slave DRL unit comprising: (i) a slave policy module receiving a single item-representation, selecting a single task entry and submitting to a slave environment for performance; (ii) a slave DRL agent configured to: (a) train the slave policy module; (b) receive an item-representation from the master DRL unit, and submit to the slave policy module; (c) receive an updated item-representation from the slave's environment, and submit the same to the master DRL unit; and (iii) the slave DRL agent.

Abstract: An automatic computer implemented method for making classification decisions to provide a desired policy that optimizes multi-objective tasks with contradicting constrains. Time and resources constrains that correspond to a predetermined level of acceptable cost are defined, as well as a cost function that represents the acceptable cost while considering the constrains. A plurality of analysis and processing modules are deployed in a computational environment, for processing data associated with the computational environment and returning results, along with indications regarding the level of confidence of the results. providing at least one agent for evaluating the results returned by each module, using a neural network being trained to dynamically determine when the level of confidence is sufficient, using one module, and if found insufficient, using more modules.

Abstract: A master-slave scheduling system, comprising (a) a master DRL unit comprising: (i) a queue containing a plurality of item-representations; (ii) a master policy module configured to select a single item-representation from the queue and submit to the slave unit; (iii) a master DRL agent configured to (a) train the master policy module; and (b) receive an updated item-representation from the slave unit, and update the queue; (b) The slave DRL unit comprising: (i) a slave policy module receiving a single item-representation, selecting a single task entry and submitting to a slave environment for performance; (ii) a slave DRL agent configured to: (a) train the slave policy module; (b) receive an item-representation from the master DRL unit, and submit to the slave policy module; (c) receive an updated item-representation from the slave's environment, and submit the same to the master DRL unit; and (iii) the slave DRL agent.