A File-Level Continuous Data Protection Scheme for Enforcing Security Baseline

Xiangxiang Jiang; Yuxi Ma; Gang Zhao; Xiaohui Kuang; Yuanzhang Li; Ruyun Zhang

doi:10.1007/978-3-030-62223-7_45

A File-Level Continuous Data Protection Scheme for Enforcing Security Baseline

Xiangxiang Jiang, Yuxi Ma, Gang Zhao, Xiaohui Kuang, Yuanzhang Li, Ruyun Zhang^*

^*Corresponding author for this work

School of Computer Science and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Massive data is the basis of machine learning, and continuous data protection is an effective means to ensure data integrity and availability. At present, continuous data protection adopts the same backup strategy in different host security environments, ignoring the potential relationship between host security state and data destruction, resulting in a low utilization rate of backup storage space. To make better use of backup storage space and improve data recovery speed, this paper proposes a file-level continuous data protection scheme for enforcing security baseline (SB-CDP). SB-CDP combines a security baseline with continuous data protection to dynamically adjust the backup strategy based on the host security status provided by the security baseline. The experimental results show that, on the one hand, SB-CDP can effectively utilize the backup storage space, on the other hand, it can effectively improve the data recovery speed and reduce the impact on the system performance.

Original language	English
Title of host publication	Machine Learning for Cyber Security - Third International Conference, ML4CS 2020, Proceedings
Editors	Xiaofeng Chen, Hongyang Yan, Qiben Yan, Xiangliang Zhang
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	519-529
Number of pages	11
ISBN (Print)	9783030622220
DOIs	https://doi.org/10.1007/978-3-030-62223-7_45
Publication status	Published - 2020
Event	3rd International Conference on Machine Learning for Cyber Security, ML4CS 2020 - Guangzhou, China Duration: 8 Oct 2020 → 10 Oct 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12486 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd International Conference on Machine Learning for Cyber Security, ML4CS 2020
Country/Territory	China
City	Guangzhou
Period	8/10/20 → 10/10/20

Keywords

File level continuous data protection
Full backup
Incremental backup
Quantitative analysis
Security baseline

Access to Document

10.1007/978-3-030-62223-7_45

Cite this

Jiang, X., Ma, Y., Zhao, G., Kuang, X., Li, Y., & Zhang, R. (2020). A File-Level Continuous Data Protection Scheme for Enforcing Security Baseline. In X. Chen, H. Yan, Q. Yan, & X. Zhang (Eds.), Machine Learning for Cyber Security - Third International Conference, ML4CS 2020, Proceedings (pp. 519-529). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12486 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-62223-7_45

Jiang, Xiangxiang ; Ma, Yuxi ; Zhao, Gang et al. / A File-Level Continuous Data Protection Scheme for Enforcing Security Baseline. Machine Learning for Cyber Security - Third International Conference, ML4CS 2020, Proceedings. editor / Xiaofeng Chen ; Hongyang Yan ; Qiben Yan ; Xiangliang Zhang. Springer Science and Business Media Deutschland GmbH, 2020. pp. 519-529 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "A File-Level Continuous Data Protection Scheme for Enforcing Security Baseline",

abstract = "Massive data is the basis of machine learning, and continuous data protection is an effective means to ensure data integrity and availability. At present, continuous data protection adopts the same backup strategy in different host security environments, ignoring the potential relationship between host security state and data destruction, resulting in a low utilization rate of backup storage space. To make better use of backup storage space and improve data recovery speed, this paper proposes a file-level continuous data protection scheme for enforcing security baseline (SB-CDP). SB-CDP combines a security baseline with continuous data protection to dynamically adjust the backup strategy based on the host security status provided by the security baseline. The experimental results show that, on the one hand, SB-CDP can effectively utilize the backup storage space, on the other hand, it can effectively improve the data recovery speed and reduce the impact on the system performance.",

keywords = "File level continuous data protection, Full backup, Incremental backup, Quantitative analysis, Security baseline",

author = "Xiangxiang Jiang and Yuxi Ma and Gang Zhao and Xiaohui Kuang and Yuanzhang Li 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 = "10.1007/978-3-030-62223-7_45",

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isbn = "9783030622220",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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}

Jiang, X, Ma, Y, Zhao, G, Kuang, X, Li, Y & Zhang, R 2020, A File-Level Continuous Data Protection Scheme for Enforcing Security Baseline. in X Chen, H Yan, Q Yan & X Zhang (eds), Machine Learning for Cyber Security - Third International Conference, ML4CS 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12486 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 519-529, 3rd International Conference on Machine Learning for Cyber Security, ML4CS 2020, Guangzhou, China, 8/10/20. https://doi.org/10.1007/978-3-030-62223-7_45

A File-Level Continuous Data Protection Scheme for Enforcing Security Baseline. / Jiang, Xiangxiang; Ma, Yuxi; Zhao, Gang et al.
Machine Learning for Cyber Security - Third International Conference, ML4CS 2020, Proceedings. ed. / Xiaofeng Chen; Hongyang Yan; Qiben Yan; Xiangliang Zhang. Springer Science and Business Media Deutschland GmbH, 2020. p. 519-529 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12486 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Jiang, Xiangxiang

AU - Ma, Yuxi

AU - Zhao, Gang

AU - Kuang, Xiaohui

AU - Li, Yuanzhang

AU - Zhang, Ruyun

PY - 2020

Y1 - 2020

N2 - Massive data is the basis of machine learning, and continuous data protection is an effective means to ensure data integrity and availability. At present, continuous data protection adopts the same backup strategy in different host security environments, ignoring the potential relationship between host security state and data destruction, resulting in a low utilization rate of backup storage space. To make better use of backup storage space and improve data recovery speed, this paper proposes a file-level continuous data protection scheme for enforcing security baseline (SB-CDP). SB-CDP combines a security baseline with continuous data protection to dynamically adjust the backup strategy based on the host security status provided by the security baseline. The experimental results show that, on the one hand, SB-CDP can effectively utilize the backup storage space, on the other hand, it can effectively improve the data recovery speed and reduce the impact on the system performance.

AB - Massive data is the basis of machine learning, and continuous data protection is an effective means to ensure data integrity and availability. At present, continuous data protection adopts the same backup strategy in different host security environments, ignoring the potential relationship between host security state and data destruction, resulting in a low utilization rate of backup storage space. To make better use of backup storage space and improve data recovery speed, this paper proposes a file-level continuous data protection scheme for enforcing security baseline (SB-CDP). SB-CDP combines a security baseline with continuous data protection to dynamically adjust the backup strategy based on the host security status provided by the security baseline. The experimental results show that, on the one hand, SB-CDP can effectively utilize the backup storage space, on the other hand, it can effectively improve the data recovery speed and reduce the impact on the system performance.

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KW - Incremental backup

KW - Quantitative analysis

KW - Security baseline

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DO - 10.1007/978-3-030-62223-7_45

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AN - SCOPUS:85097135524

SN - 9783030622220

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Machine Learning for Cyber Security - Third International Conference, ML4CS 2020, Proceedings

A2 - Chen, Xiaofeng

A2 - Yan, Hongyang

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PB - Springer Science and Business Media Deutschland GmbH

T2 - 3rd International Conference on Machine Learning for Cyber Security, ML4CS 2020

Y2 - 8 October 2020 through 10 October 2020

ER -

Jiang X, Ma Y, Zhao G, Kuang X, Li Y, Zhang R. A File-Level Continuous Data Protection Scheme for Enforcing Security Baseline. In Chen X, Yan H, Yan Q, Zhang X, editors, Machine Learning for Cyber Security - Third International Conference, ML4CS 2020, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. p. 519-529. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-62223-7_45

A File-Level Continuous Data Protection Scheme for Enforcing Security Baseline

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Keywords

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