The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Towards Achieving Data Security with the Cloud Computing Adoption Framework
Open accessAbstract
Offering real-time data security for petabytes of data is important for cloud computing. A recent survey on cloud security states that the security of users' data has the highest priority as well as concern. We believe this can only be able to achieve with an approach that is systematic, adoptable and well-structured. Therefore, this paper has developed a framework known as Cloud Computing Adoption Framework (CCAF) which has been customized for securing cloud data. This paper explains the overview, rationale and components in the CCAF to protect data security. CCAF is illustrated by the system design based on the requirements and the implementation demonstrated by the CCAF multi-layered security. Since our Data Center has 10 petabytes of data, there is a huge task to provide real-time protection and quarantine. We use Business Process Modeling Notation (BPMN) to simulate how data is in use. The use of BPMN simulation allows us to evaluate the chosen security performances before actual implementation. Results show that the time to take control of security breach can take between 50 and 125 hours. This means that additional security is required to ensure all data is well-protected in the crucial 125 hours. This paper has also demonstrated that CCAF multi-layered security can protect data in real-time and it has three layers of security: 1) firewall and access control; 2) identity management and intrusion prevention and 3) convergent encryption. To validate CCAF, this paper has undertaken two sets of ethical-hacking experiments involved with penetration testing with 10,000 trojans and viruses. The CCAF multi-layered security can block 9,919 viruses and trojans which can be destroyed in seconds and the remaining ones can be quarantined or isolated. The experiments show although the percentage of blocking can decrease for continuous injection of viruses and trojans, 97.43 percent of them can be quarantined. Our CCAF multi-layered security has an average of 20 percent better performance than the single-layered approach which could only block 7,438 viruses and trojans. CCAF can be more effective when combined with BPMN simulation to evaluate security process and penetrating testing results.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., et al. (2010) · Communications of the ACM · doi:10.1145/1721654.1721672 · Cited by 9,464
Above the Clouds: A Berkeley View of Cloud Computing
Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R. H., Konwinski, A., Lee, G., Patterson, D. A., Rabkin, A., Stoica, I. G., et al. (2009) · Cited by 5,796
Attribute-based encryption for fine-grained access control of encrypted data
Goyal, V., Pandey, O., Sahai, A., & Waters, B. (2006) · Proceedings of the 13th ACM conference on Computer and communications security · doi:10.1145/1180405.1180418 · Cited by 5,009
Cloud computing: state-of-the-art and research challenges
Zhang, Q., Cheng, L., & Boutaba, R. (2010) · Journal of Internet Services and Applications · doi:10.1007/s13174-010-0007-6 · Cited by 3,515
Cloud computing — The business perspective
Marston, S., Li, Z., Bandyopadhyay, S., Zhang, J., & Ghalsasi, A. (2011) · Decision Support Systems · doi:10.1016/j.dss.2010.12.006 · Cited by 2,364
Addressing cloud computing security issues
Zissis, D., & Lekkas, D. (2012) · Future Generation Computer Systems · doi:10.1016/j.future.2010.12.006 · Cited by 1,842
The cost of a cloud: research problems in data center networks
Greenberg, A., Hamilton, J., Maltz, D. A., & Patel, P. (2008) · ACM SIGCOMM Computer Communication Review · doi:10.1145/1496091.1496103 · Cited by 1,621
Achieving Secure, Scalable, and Fine-grained Data Access Control in Cloud Computing
Yu, S., Wang, C., Ren, K., & Lou, W. (2010) · 2010 Proceedings IEEE INFOCOM · doi:10.1109/infcom.2010.5462174 · Cited by 1,602
Use of Electronic Health Records in U.S. Hospitals
Jha, A. K., DesRoches, C. M., Campbell, E. G., Donelan, K., Rao, S. R., Ferris, T. G., Shields, A., Rosenbaum, S., & Blumenthal, D. (2009) · New England Journal of Medicine · doi:10.1056/nejmsa0900592 · Cited by 1,473
Cloud Computing � Issues, Research and Implementations
Vouk, M. A. (2008) · Journal of Computing and Information Technology · doi:10.2498/cit.1001391 · Cited by 860
Cited by
A blockchain future for internet of things security: a position paper
Banerjee, M., Lee, J., & Choo, K.-K. R. (2018) · Digital Communications and Networks · doi:10.1016/j.dcan.2017.10.006 · Cited by 585
Gai, K., Qiu, M., & Sun, X. (2018) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2017.10.011 · Cited by 566
Multi-key privacy-preserving deep learning in cloud computing
Li, P., Li, J., Huang, Z., Li, T., Gao, C.-Z., Yiu, S.-M., & Chen, K. (2017) · Future Generation Computer Systems · doi:10.1016/j.future.2017.02.006 · Cited by 462
A review and future direction of agile, business intelligence, analytics and data science
Larson, D., & Chang, V. (2016) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2016.04.013 · Cited by 427
Self-citationFog computing security: A review of current applications and security solutions
Khan, S., Parkinson, S., & Qin, Y. (2017) · Journal of Cloud Computing · doi:10.1186/s13677-017-0090-3 · Cited by 409
Metadata
- Authors
- Chang, V., & Ramachandran, M.
- Venue
- IEEE Transactions on Services Computing
- Published
- 2016-01-01
- Volume
- 9
- Issue
- 1
- Pages
- 138-151
- ISSN
- 1939-1374 (ISSN-L)
- 2372-0204
- OpenAlex
- W2275132005
- Cited by
- 286
- References
- 23
- FWCI
- 108.11
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Towards Achieving Data Security with the Cloud Computing Adoption Framework. https://app.cochat.ai/papers/10.1109/tsc.2015.2491281
- The paper
- Chang, V., & Ramachandran, M. (2016). Towards Achieving Data Security with the Cloud Computing Adoption Framework. IEEE Transactions on Services Computing. https://doi.org/10.1109/tsc.2015.2491281
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.