18215422. DATA DETECTION METHOD AND ELECTRONIC DEVICE simplified abstract (Dell Products L.P.)
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DATA DETECTION METHOD AND ELECTRONIC DEVICE
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DATA DETECTION METHOD AND ELECTRONIC DEVICE - A simplified explanation of the abstract
This abstract first appeared for US patent application 18215422 titled 'DATA DETECTION METHOD AND ELECTRONIC DEVICE
The abstract of this patent application describes a data detection technique that involves determining count features corresponding to multiple time points based on data blocks of a storage object. It further involves calculating correlation coefficients between count features for different time points to detect potential attacks on the storage object.
- Detecting count features for multiple time points based on data blocks of a storage object
- Calculating correlation coefficients between count features for different time points
- Identifying potential attacks on the storage object if the calculated score falls below a predetermined threshold
Potential Applications: - Cybersecurity systems - Data protection software - Malware detection tools
Problems Solved: - Efficiently detecting attacks on storage systems - Quick identification of malware threats - Enhancing data security measures
Benefits: - Improved checking efficiency - Cost savings on security measures - Quick recovery of stored data
Commercial Applications: Title: Advanced Data Security System for Storage Objects This technology can be utilized in various industries such as finance, healthcare, and government agencies to protect sensitive data from cyber threats. It can also be integrated into cloud storage services to enhance data security for businesses and individuals.
Questions about Data Detection Techniques: 1. How does this technology improve data security measures for storage systems? This technology enhances data security by efficiently detecting potential attacks on storage objects and enabling quick recovery of stored data in case of a security breach.
2. What are the key features of this data detection technique? The key features include determining count features for multiple time points, calculating correlation coefficients between these features, and identifying potential attacks based on the calculated scores.
Original Abstract Submitted
Data detection techniques involve: determining count features respectively corresponding to a plurality of time points based on data blocks of a storage object; determining, for a first time point among the plurality of time points and according to the count features respectively corresponding to the plurality of time points, a plurality of corresponding correlation coefficients between a count feature for the first time point and count features for the other time points among the plurality of time points; and determining, if a score calculated according to the plurality of correlation coefficients is less than a predetermined threshold, that the storage object corresponding to the first time point is under attack. Accordingly, a storage system can be protected, and the point in time when it is attacked by malware can be quickly located, which improves checking efficiency, saves checking costs, and helps users to quickly recover stored data.
