@inproceedings{484b69d0447d4847a5fd7f8b109bc058,
title = "An Adaptive Data Protection Scheme for Optimizing Storage Space",
abstract = "Data is the main driving factor of artificial intelligence represented by machine learning, and how to ensure data security is one of the severe challenges. In many traditional methods, a single snapshot strategy is used to protect data. In order to meet the flexibility of data protection and optimize storage space, this paper presents a new architecture and an implementation in the Linux kernel. The idea is to hook system calls and analyze the relationship between applications and files. By tracking system calls, the system can perceive the file modification and automatically adjust the time interval for generating snapshots. Time granularity changes with the application load to achieve on-demand protection. Extensive experiments have been carried out to show that the scheme can monitor the process of operating files, reduce storage costs and hardly affect the performance of system.",
keywords = "Adaptive protection, Snapshot, Storage optimization",
author = "Meng Ming and Gang Zhao and Xiaohui Kuang and Lu Liu and Ruyun Zhang",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 3rd International Conference on Machine Learning for Cyber Security, ML4CS 2020 ; Conference date: 08-10-2020 Through 10-10-2020",
year = "2020",
doi = "10.1007/978-3-030-62460-6_22",
language = "English",
isbn = "9783030624590",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "250--260",
editor = "Xiaofeng Chen and Hongyang Yan and Qiben Yan and Xiangliang Zhang",
booktitle = "Machine Learning for Cyber Security - Third International Conference, ML4CS 2020, Proceedings",
address = "Germany",
}