According to some embodiments, a method for training a system for data traffic analysis is described, the system comprising a deep learning algorithm, wherein the deep learning algorithm comprises a prediction model which is trained to take into account the history of data. According to some embodiments, the deep learning algorithm is operated on a graphical processing unit. According to some embodiments, the system for data traffic analysis is configured to detect anomalies in the data, based also on past data. According to some embodiments, the system for data traffic analysis is configured to simultaneously detect anomalies in the data and update its prediction model. Additional methods and systems in the field of data traffic analysis are also described. According to some embodiments, data of a car are analyzed in order to detect anomalies.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for training a system for analysis of data traffic of a device, the system including at least a deep learning algorithm operable on at least a processing unit, the method comprising: feeding a training set of data to the system for data traffic analysis, attempting to reconstruct at least part of the training set of data with the deep learning algorithm, the reconstruction taking into account past data of the training set, and based on at least this attempted reconstruction, updating a model of the deep learning algorithm, for allowing the system for data traffic analysis to detect anomalies in the data of the device. 2. The method of claim 1, wherein the processing unit comprises at least a graphical processing unit. 3. The method of claim 1, wherein the training is performed: while the device is used, and/or before the device is being used. 4. The method of claim 1, wherein the device is a car. 5. The method of claim 1, wherein the deep learning algorithm comprises at least an input layer, an output layer, and hidden layers in which neurons receive at least past data, and wherein the reconstruction comprises attempting to reconstruct data fed to the input layer at the output layer. 6. A method for training a system for analysis of data traffic of a device, the system including at least a deep learning algorithm operable on at least a processing unit, the method comprising: feeding a training set of data to the system for traffic analysis, attempting to reconstruct at least part of the training set of data with the deep learning algorithm, wherein the deep learning algorithm is operated on at least a graphical processing unit, and based on at least this attempted reconstruction, updating a model of the deep learning algorithm, for allowing the system for data traffic analysis to detect anomalies in the data of the device. 7. The method of claim 6, wherein the reconstruction takes into account past data of the training set. 8. The method of claim 6, wherein the training is performed: while the device is being used, and/or before the device is being used. 9. The method of claim 6, wherein the device is a car. 10. The method of claim 6, wherein the deep learning algorithm comprises at least an input layer, an output layer, and hidden layers in which neurons receive at least past data, and wherein the reconstruction comprises attempting to reconstruct data fed to the input layer at the output layer. 11. A method for analyzing data traffic of a device with a system for data traffic analysis, the system for traffic analysis comprising at least a deep learning algorithm operable on at least a processing unit, the method comprising: feeding a plurality of data exchanged in the device to the system for data traffic analysis, attempting to reconstruct at least part of said plurality of data with the deep learning algorithm, the reconstruction taking into account past data, comparing the reconstructed data with at least part of said plurality of data, providing indications on anomalies in the data, based on at least said comparison. 12. The method of claim 11, wherein the processing unit comprises at least a graphical processing unit. 13. The method of claim 11, wherein the deep learning algorithm is trained while the device is used and/or before the device is used. 14. The method of claim 11, wherein the device is a car. 15. The method of claim 11, wherein the deep learning algorithm comprises at least an input layer, an output layer, and hidden layers in which neurons receive at least past data, and wherein the reconstruction comprises attempting to reconstruct data fed to the input layer at the output layer. 16. A method for analyzing data traffic of a device with a system for data traffic analysis, the system for traffic analysis comprising at least a deep learning algorithm operable on at least a processing unit, the method comprising: feeding a plurality of data exchanged in the device to the system for traffic analysis, attempting to reconstruct at least part of said plurality of data with the deep learning algorithm, wherein the deep learning algorithm is operated on a graphical processing unit, comparing the reconstructed data with at least part of said plurality of data, and providing indications on anomalies in the data, based on at least said comparison. 17. The method of claim 16, wherein the reconstruction takes into account past data. 18. The method of claim 16, wherein the deep learning algorithm is trained while the device is used and/or before the device is used. 19. The method of claim 16, wherein the device is a car. 20. The method of claim 16, wherein the deep learning algorithm comprises at least an input layer, an output layer, and hidden layers in which neurons receive at least past data, and wherein the reconstruction comprises attempting to reconstruct data fed to the input layer at the output layer. 21. A system for analyzing data traffic of a device, the system comprising: at least a processing unit, at least a deep learning algorithm operable on said processing unit, the system being configured to: receive a plurality of data of the device, attempt to reconstruct at least part of the plurality of data with the deep learning algorithm, the reconstruction taking into account past data, compare the reconstructed data with at least part of the plurality of data, and provide indications on anomalies in the data, based on at least said comparison. 22. The system of claim 21, wherein the processing unit comprises at least a graphical processing unit. 23. The system of claim 21, wherein the deep learning algorithm is trained while the device is used and/or before the device is used. 24. The system of claim 21, wherein the device is a car. 25. The system of claim 21, wherein the deep learning algorithm comprises at least an input layer, an output layer, and hidden layers in which neurons receive at least past data, and wherein the reconstruction comprises attempting to reconstruct data fed to the input layer at the output layer. 26. A system for analyzing data traffic of a device, the system comprising: at least a graphical processing unit, at least a deep learning algorithm operable on said graphical processing unit, the system being configured to: receive a plurality of data of the device, attempt to reconstruct at least part of the plurality of data with the deep learning algorithm, compare the reconstructed data with at least part of the plurality of data, and provide indications on anomalies in the data, based on at least said comparison. 27. The system of claim 26, wherein the reconstruction takes into account past exchanged data. 28. The system of claim 26, wherein the deep learning algorithm is trained while the device is used and/or before the device is used. 29. The system of claim 26, wherein the device is a car. 30. The system of claim 26, wherein the deep learning algorithm comprises at least an input layer, an output layer, and hidden layers in which neurons receive at least past data, and wherein the reconstruction comprises attempting to reconstruct data fed to the input layer at the output layer. 31. A training module, for training a system for analyzing data traffic of a device, the system for traffic analysis comprising at least a deep learning algorithm, the training module being configured to: feed a training set of data to the system for traffic analysis, cause the system for data traffic analysis to: attempt to reconstruct at least part of the training set of data with the deep learning algorithm, wherein the deep learning algorithm is operated on at least a graphical processing unit, and based on this reconstruction, update a model of the deep learning algorithm, for allowing the system for data traffic analysis to detect anomalies in the data of the device. 32. The training module of claim 31, wherein the reconstruction takes into account past exchanged data. 33. A non-transitory storage device readable by a machine, tangibly embodying a program of instructions executable by the machine to perform a method for training a system for analysis of data traffic of a device, the system including at least a deep learning algorithm operable on at least a processing unit, the method comprising: feeding a training set of data to the system for data traffic analysis, attempting to reconstruct at least part of the training set of data with the deep learning algorithm, the reconstruction taking into account past data of the training set, and based on at least this attempted reconstruction, updating a model of the deep learning algorithm, for allowing the system for data traffic analysis to detect anomalies in the data of the device. 34. A non-transitory storage device readable by a machine, tangibly embodying a program of instructions executable by the machine to perform a method for analyzing data traffic of a device with a system for data traffic analysis, the system for traffic analysis comprising at least a deep learning algorithm operable on at least a processing unit, the method comprising: feeding a plurality of data exchanged in the device to the system for data traffic analysis, attempting to reconstruct at least part of said plurality of data with the deep learning algorithm, the reconstruction taking into account past exchanged data, comparing the reconstructed data with at least part of said plurality of data, providing indications on anomalies in the data, based on at least said comparison.