Recognize corrupted data packeted while transferring data through ensemble machine learning techniques

In today's world, every technology is moving towards cloud storage which makes file transfer protocols a cornerstone for any platform to run smoothly. Therefore, identifying damaged files is a crucial responsibility in the area of data management and integrity. In this study, we suggest an AdaBoost-based machine learning technique for identifying damaged files. AdaBoost is an ensemble method that combines many weak classifiers into one powerful classifier. In our method, we train weak classifiers called decision stumps using a dataset that includes both damaged and healthy files. The final prediction was decided by a weighted majority vote of all the weak classifiers. We evaluated our method on a dataset generated by collecting metadata information of files and passed it to the algorithms. We used the AdaBoost approach as a base algorithm for comparison along with more established techniques like Naive Bayes, Logistic Regression, and Linear Discriminant Analysis. The results show that the AdaBoost algorithm is effective in detecting corrupted files, and it performs better than other traditional methods. Additionally, our method is computationally efficient and can be easily integrated into existing data management systems. It is expected to have a positive impact on data integrity and management in various fields such as digital forensics, cloud computing, and storage systems.