Embodiments are directed to a computer implemented method for generating a drift detector. The method includes generating, using a processor system, drift cases based at least in part on known drift set data of a computer system. The method further includes injecting, using the processor system, the drift cases into the computer system to generate a first data set. The method further includes applying, using the processor system, cleaning rules to the first data set to reduce a size of the first data set and generate a cleaned data set. The method further includes extracting one or more features of the cleaned data set. The method further includes normalizing the extracted one or more features of the cleaned data set. The method further includes training a machine learning system using the extracted and normalized one or more features of the cleaned data, wherein an output of the machine learning system comprises the drift detector.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer implemented method for generating a drift detector, the method comprising: generating, using a processor system, drift cases based at least in part on known drift set data of a computer system; injecting, using the processor system, the drift cases into the computer system to generate a first data set; applying, using the processor system, cleaning rules to the first data set to reduce a size of the first data set and generate a cleaned data set; extracting one or more features of the cleaned data set; normalizing the one or more features of the cleaned data set; and training a machine learning system using the extracted and normalized one or more features of the cleaned data; wherein an output of the machine learning system comprises the drift detector. 2. The computer implemented method of claim 1, wherein the drift cases comprise positive drift cases and negative drift cases. 3. The computer implemented method of claim lfurther comprising injecting, using the processor system, one or more normal changes into the computer system. 4. The computer implemented method of claim 1, wherein the cleaning rules are domain independent. 5. The computer implemented method of claim 1, wherein the one or more features comprise: a change object; a change characteristic; or a change sequences. 6. The computer implemented method of claim 1, wherein the machine learning system comprises an artificial neural network (ANN). 7. The computer implemented method of claim 1, wherein the machine learning system comprises a support vector machine (SVM). 8. A computer system for generating a drift detector, the system comprising: a memory; and a processor system communicatively coupled to the memory; wherein the processor system configured to perform a method comprising: generating drift cases based at least in part on known drift set data of a computer system; injecting the drift cases into the computer system to generate a first data set; applying cleaning rules to the first data set to reduce a size of the first data set and generate a cleaned data set; extracting one or more features of the cleaned data set; normalizing the one or more features of the cleaned data set; and training a machine learning system using the extracted and normalized one or more features of the cleaned data; wherein an output of the machine learning system comprises the drift detector. 9. The computer system of claim 8, wherein the drift cases comprise positive drift cases and negative drift cases. 10. The computer system of claim 8, wherein the method further comprises injecting one or more normal changes into the computer system. 11. The computer system of claim 8, wherein the cleaning rules are domain independent. 12. The computer system of claim 8, wherein the one or more features comprise: a change object; a change characteristic; or a change sequences. 13. The computer system of claim 8, wherein the machine learning system comprises an artificial neural network (ANN). 14. The computer system of claim 8, wherein the machine learning system comprises a support vector machine (SVM). 15. A computer program product for generating a drift detector, the computer program product comprising: a computer readable storage medium having program instructions embodied therewith, wherein the computer readable storage medium is not a transitory signal per se, the program instructions readable by a processor system to cause the processor system to perform a method comprising: generating drift cases based at least in part on known drift set data of a computer system; injecting the drift cases into the computer system to generate a first data set; applying cleaning rules to the first data set to reduce a size of the first data set and generate a cleaned data set; extracting one or more features of the cleaned data set; normalizing the one or more features of the cleaned data set; and training a machine learning system using the extracted and normalized one or more features of the cleaned data; wherein an output of the machine learning system comprises the drift detector. 16. The computer program product of claim 15, wherein the drift cases comprise positive drift cases and negative drift cases. 17. The computer program product of claim 15, wherein the method further comprises injecting one or more normal changes into the computer system. 18. The computer program product of claim 15, wherein the cleaning rules are domain independent. 19. The computer program product of claim 15, wherein the one or more features comprise: a change object; a change characteristic; or a change sequences. 20. The computer program product of claim 15, wherein the machine learning system comprises: an artificial neural network (ANN); or a support vector machine (SVM).