A computerized method of classifying network accessible storage transactions at network accessible storage. The method comprises obtaining an client predictive security model for anomaly or malfunctioning detection, the client predictive security model is dynamically created by an analysis of a plurality of client transactions made to access target data stored in an client computing device, monitoring a plurality of network accessible storage transactions made to access a replica of the target data when the replica is stored in an network accessible storage, and classifying at least some of the plurality of network accessible storage transactions based on the client predictive security model.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computerized method of classifying network accessible storage transactions at network accessible storage, comprising: obtaining an client predictive security model for anomaly or malfunctioning detection, the client predictive security model being dynamically created by an analysis of a plurality of client transactions made to access target data stored in an client computing device; monitoring a plurality of network accessible storage transactions made to access a replica of the target data when the replica is stored in an network accessible storage; and classifying at least some of the plurality of network accessible storage transactions based on the client predictive security model. 2. The computerized method of claim 1, wherein the client predictive security model comprises a classifier calculated using tagged data for classifying security events. 3. The computerized method of claim 1, wherein the client predictive security model comprises or being created based on at least one baseline for anomaly or malfunctioning detection. 4. The computerized method of claim 1, wherein the on-premises security model is created by an additional analysis of at least one member of a group consisting of collects access logs, maintenance logs, deployment logs and/or configuration logs associated with the target data. 5. The computerized method of claim 1, further comprising classifying the replica based on an analysis of a plurality of features of the target data; wherein the client predictive security model is selected from a plurality of client predictive security models created according to the analysis of a plurality of client transactions. 6. The computerized method of claim 5, wherein the plurality of features comprises at least one member of a group consisting of data volume accesses, read transactions data, write transactions data, source internet protocol (IP) used for login actions, time of login actions, credentials, user privilege level data, time of accessing data, and frequency of accessing data. 7. The computerized method of claim 1, further comprising clustering a plurality of datasets from the target data into a plurality of dataset clusters based on unsupervised learning techniques and classifying the replica accordingly. 8. The computerized method of claim 1, further comprising creating an network accessible storage data model by adapting the client predictive security model according to an analysis of the plurality of network accessible storage transactions. 9. The computerized method of claim 8, wherein the classifying comprises selecting one of a plurality of client predictive security models, the network accessible storage data model, and a combination of the client predictive security model and the network accessible storage data model based on abnormality detection rules. 10. The computerized method of claim 8, wherein the classifying comprises selecting one of the client predictive security model, the network accessible storage data model, and a combination of the client predictive security model and the network accessible storage data model based on historical performances of the client predictive security model, the network accessible storage data model, and the combination. 11. The computerized method of claim 8, wherein the classifying is based on a weighted function that takes into account an outcome of the client predictive security model and the network accessible storage data model. 12. The computerized method of claim 8, wherein the classifying is adjusted according a user input acquired using a graphical user interface. 13. A system, comprising: at least one processor; and memory including computer-executable instructions that, based on execution by the at least one processor, configure the at least one processor to: obtain an client predictive security model for anomaly or malfunctioning detection, the client predictive security model is dynamically created by an analysis of a plurality of client transactions made to access target data stored in an client computing device; monitor a plurality of network accessible storage transactions made to access a replica of the target data when the replica is stored in an network accessible storage; and classify at least some of the plurality of network accessible storage transactions based on the client predictive security model. 14. The system of claim 13, wherein the client predictive security model comprises a classifier calculated using tagged data for classifying security events. 15. The system of claim 13, wherein the client predictive security model comprises or being created based on at least one baseline for anomaly or malfunctioning detection. 16. The system of claim 13, wherein the on-premises security model is created by an additional analysis of at least one member of a group consisting of collects access logs, maintenance logs, deployment logs and/or configuration logs associated with the target data. 17. The system of claim 13, wherein the at least one processor is adapted to cluster a plurality of datasets from the target data into a plurality of dataset clusters based on unsupervised learning techniques and to perform the classification of the replica accordingly. 18. The system of claim 13, wherein the at least one processor is adapted to creates an network accessible storage data model by adapting the client predictive security model according to an analysis of the plurality of network accessible storage transactions. 19. The system of claim 13, wherein the at least one processor is adapted to classify the at least some network accessible storage transactions is based on a weighted function that takes into account an outcome of the client predictive security model and the network accessible storage data model. 20. A software program product, comprising: a non-transitory computer readable storage medium; first program instructions to obtain an client predictive security model for anomaly or malfunctioning detection, the client predictive security model is dynamically created by an analysis of a plurality of client transactions made to access target data stored in an client computing device; second program instructions to monitor a plurality of network accessible storage transactions made to access a replica of the target data when the replica is stored in an network accessible storage; and third program instructions to classify at least some of the plurality of network accessible storage transactions based on the client predictive security model; wherein the first, second, and third program instructions are executed by at least one computerized processor from the non-transitory computer readable storage medium.