Detection Method of Data Integrity in Network Storage Based on Symmetrical Difference

Abstract

In order to enhance the recall and the precision performance of data integrity detection, a method to detect the network storage data integrity based on symmetric difference was proposed. Through the complete automatic image annotation system, the crawler technology was used to capture the image and related text information. According to the automatic word segmentation, pos tagging and Chinese word segmentation, the feature analysis of text data was achieved. Based on the symmetrical difference algorithm and the background subtraction, the feature extraction of image data was realized. On the basis of data collection and feature extraction, the sentry data segment was introduced, and then the sentry data segment was randomly selected to detect the data integrity. Combined with the accountability scheme of data security of the trusted third party, the trusted third party was taken as the core. The online state judgment was made for each user operation. Meanwhile, credentials that cannot be denied by both parties were generated, and thus to prevent the verifier from providing false validation results. Experimental results prove that the proposed method has high precision rate, high recall rate, and strong reliability.

Data poisoning attacks detection based on the convergence of the Peter and Clark algorithm and Bayesian adversarial federated learning

“…There have not been many studies on manipulating ML methods of energy consumption data, despite adversarial ML, and attacks on ML methods, being a developing research field. 5 Early data poisoning research used manual methods to create fake users, who typically perform suboptimally in attacks even when they have access to input data as well as are aware of the recommendation system. 6 For instance, a random attack selects filler items at random for every fake user as well as rates filler items according to the normal distribution of all rating information.…”

“…Many application domains, including worm signature generation, denial-of-service (DoS) attack detection, portable document format (PDF) malware classification, etc., have examined these types of attacks. There have not been many studies on manipulating ML methods of energy consumption data, despite adversarial ML, and attacks on ML methods, being a developing research field 5 . Early data poisoning research used manual methods to create fake users, who typically perform suboptimally in attacks even when they have access to input data as well as are aware of the recommendation system 6 .…”