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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Jacob, S., Menon, V.G., Al-Turjman, F., et al.: Artificial muscle intelligence system with deep learning for post-stroke assistance and rehabilitation. IEEE Access 7, 133463–133473 (2019)
Sangaiah, A.K., Lu, H., Hu, Q.: Cognitive science and artificial intelligence for human cognition and communication. IEEE Consum. Electron. Mag. 9(1), 72–73 (2019)
Zafar, S., Jangsher, S., Aloqaily, M., et al.: Resource allocation in moving small cell network using deep learning based interference determination. In: 2019 IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC). IEEE, pp. 1–6 (2019)
Sharma, G., Srivastava, G., Mago, V.: A framework for automatic categorization of social data into medical domains. IEEE Trans. Comput. Soc. Syst. 7, 129–140 (2019)
Edwards, L.: data protection and e-privacy: from spam and cookies to big data, machine learning and profiling. Soc. Sci. Electron. Publ. (2018)
Ullah, F., Naeem, H., Jabbar, S., et al.: Cyber security threats detection in internet of things using deep learning approach. IEEE Access 7, 124379–124389 (2019)
Elnozahy, E.N., Alvisi, L., Wang, Y.M., et al.: A survey of rollback-recovery protocols in message-passing systems. ACM Comput. Surv. (CSUR) 34(3), 375–408 (2002)
Netzer, R.H.B., Xu, J.: Necessary and sufficient conditions for consistent global snapshots. IEEE Trans. Parallel Distrib. Syst. 6(2), 165–169 (1995)
Johann, T.: Reward for return of sheepdog and pups. Farmers Weekly 169(7), 1 (2018)
Chen, L., Kang, H., Jia, W.: Design and implementation of snapshot system in cloud storage log-structured file-system. Comput. Appl. Softw. 7 (2013)
Zhao, Z., Luo, Y.: Design and implementation of a remote disaster recovery system based on fine-grained snapshot. Comput. Eng. Sci. 7 (2008)
Zhou, W., Tan, H., Yi, L., et al.: High-performance snapshot of logical volumes based on out-of-band storage virtualization. J. Comput. Res. Dev. 49(3), 636–645 (2012)
Li, C., Zhang, Q., Tan, J., Yan, Z.: Linux file system encryption design. Internet Things Technol. 8(2): 77–79, 82 (2018)
Peterson, Z., Burns, R.: Ext3cow: a time-shifting file system for regulatory compliance. ACM Trans. Storage (TOS) 1(2), 190–212 (2005)
Hitz, D., Lau, J., Malcolm, M.A.: File system design for an NFS file server appliance. USENIX Winter 94 (1994)
Duzy, G.: Match snaps to apps. Storage, Special Issue on Managing the Information that Drives the Enterprise, 46–52 (2005)
Xu, G., Gang, W., Jing, L.: Multi-point incremental snapshot design based on the same snapshot volume. Comput. Eng. Appl. 3, 113–115 (2005)
Bhat, W.A., Wani, M.A.: Forensic analysis of B-tree file system (Btrfs). Digit. Investig. 27, 57–70 (2018)
Wei, Y., Shin, D.: NAND flash storage device performance in Linux file system. In: 2011 6th International Conference on Computer Sciences and Convergence Information Technology (ICCIT). IEEE (2011)
Li, S., et al.: COW-IMM: a novel integrity measurement method based on copy-on-write for file in virtual machine. IEEE Access 6, 51776–51790 (2018)
Guo, S., Xie, W.: Analysis in depth on Linux proc file system programming. J. Huaqiao Univ. (Nat. Sci.) 5 (2010)
Peng, G.-J., et al.: Abnormal file management activities identification system based on association analysis of behaviors. Comput. Eng. Des. (2015)
Acknowledgements
This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFB1004402, National Natural Science Foundation of China (No. U1936218, No. 61876019), and Zhejiang Lab (No. 2020LE0AB02).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Ming, M., Zhao, G., Kuang, X., Liu, L., Zhang, R. (2020). An Adaptive Data Protection Scheme for Optimizing Storage Space. In: Chen, X., Yan, H., Yan, Q., Zhang, X. (eds) Machine Learning for Cyber Security. ML4CS 2020. Lecture Notes in Computer Science(), vol 12487. Springer, Cham. https://doi.org/10.1007/978-3-030-62460-6_22
Download citation
DOI: https://doi.org/10.1007/978-3-030-62460-6_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-62459-0
Online ISBN: 978-3-030-62460-6
eBook Packages: Computer ScienceComputer Science (R0)