TARU PUBLICATIONS
Journal of Discrete Mathematical Sciences and Cryptography cover

Journal of Discrete Mathematical Sciences and Cryptography

(Since 1998)
Open Access ·Peer-reviewed·ISSN (Online): 2169-0065·ISSN (Print): 0972-0529

CiteScore 2025: 2.7 (Q1)

Powered by:Powered by

Monthly Journal: Publishes theoretical and applied research in all areas of Discrete Mathematical Sciences, Cryptography, Combinatorics, Elliptic Curves and Information Security.

Issues up to 2022 co-published with and available at:Taylor & Francis Online
submissions@tarupublications.com
Taru Online First Current IssueSpecial IssuesBrowse all Issues
Journal of Discrete Mathematical Sciences and Cryptography
Open Access Research Article

Secure and efficient storage approach for cloud environment using hybrid machine learning techniques

* , ,

* Corresponding author · click or hover a name for details

pp. 1027–1043Vol. 27Issue 3April 2024DOI: 10.1080/09720529.2022.2152563 Crossmark XML
Published Online:
03 Apr 2024
Article type:
Research Article
Language:
EN
Article no.:
JDMSC-1716
Pages:
1027–1043

Abstract

Cloud computing is a new way to organize things. Cloud users can store their files that are far away and linked to the web. As a result, personal and critical data are stored in an easily accessible and transferable manner. Hence, its demand is expanding daily. Due to the fact that the user accesses data over the internet, issues about the secrecy and confidentiality have arisen. To address this issue, hybridization employs an encryption technology known as the RSA with 3- DES approach. This article primarily addressed two aspects: data security and storage. The encryption strategy is used to secure cloud data, while the MBFD with WOA & ANN approach is used to store it. Also the utilization of neural network along with the whale optimization model ensures the controlled storage of cloud has been done. The experiment demonstrates that the suggested cloud system outperforms the present one in terms of energy usage, latency, and SLA breach.

Keywords

Service level agreementArtificial neural networkData encryption standardMachine learningOptimization

Subject Classifications

68

References

[1] Sun, X., Wang, Z., Wu, Y., Che, H., & Jiang, H. A price-aware congestion control protocol for cloud services. Journal of Cloud Computing, 10(1), 1-15 (2021).
[2] Al-Said Ahmad, A., & Andras, P. Scalability resilience framework using application-level fault injection for cloud-based software services. Journal of Cloud Computing, 11(1), 1-13 (2022).
[3] Butt, U. A., Mehmood, M., Shah, S. B. H., Amin, R., Shaukat, M. W., Raza, S. M., ... & Piran, M. A review of machine learning algorithms for cloud computing security. Electronics, 9(9), 1379 (2020).
[4] Butt, U. A., Mehmood, M., Shah, S. B. H., Amin, R., Shaukat, M. W., Raza, S. M., ... & Piran, M. A review of machine learning algorithms for cloud computing security. Electronics, 9(9), 1379 (2020).
[5] Duc, T. L., Leiva, R. G., Casari, P., & Östberg, P. O. Machine learning methods for reliable resource provisioning in edge-cloud computing: A survey. ACM Computing Surveys (CSUR), 52(5), 1-39 (2019).
[6] Li, K., Gibson, C., Ho, D., Zhou, Q., Kim, J., Buhisi, O., ... & Gerber, M. Assessment of machine learning algorithms in cloud computing frameworks. In 2013 IEEE Systems and Information Engineering Design Symposium, pp. 98-103. IEEE (2013, April).
[7] Callara, M., & Wira, P. User behavior analysis with machine learning techniques in cloud computing architectures. In 2018 International Conference on Applied Smart Systems (ICASS), pp. 1-6. IEEE (2018, November).
[8] Oh, M., Park, S., Kim, S., & Chae, H. Machine learning-based analysis of multi-omics data on the cloud for investigating gene regulations. Briefings in bioinformatics, 22(1), 66-76 (2021).
[9] Lee, B. H., Dewi, E. K., & Wajdi, M. F. Data security in cloud computing using AES under HEROKU cloud. In 2018 27th wireless and optical communication conference (WOCC), pp. 1-5. IEEE (2018, April).
[10] Ebrahim, M. A., El-Maddah, I. A., & Mohamed, H. K. Hybrid model for cloud data security using steganography. In 2017 12th International Conference on Computer Engineering and Systems (ICCES), pp. 135-140. IEEE (2017, December).
[11] Timothy, D. P., & Santra, A. K. A hybrid cryptography algorithm for cloud computing security. In 2017 International conference on microelectronic devices, circuits and systems (ICMDCS), pp. 1-5. IEEE (2017, August).
[12] Amalarethinam, I. G., & Leena, H. M. Enhanced RSA algorithm with varying key sizes for data security in cloud. In 2017 World Congress on Computing and Communication Technologies (WCCCT), pp. 172-175. IEEE (2017, February).
[13] Ardy, R. D., Indriani, O. R., Sari, C. A., & Rachmawanto, E. H., Digital image signature using triple protection cryptosystem (RSA, Vigenere, and MD5). In 2017 International Conference on Smart Cities, Automation & Intelligent Computing Systems (ICON-SONICS), pp. 87-92. IEEE (2017, November).
[14] Arora, A., Khanna, A., Rastogi, A., & Agarwal, A. Cloud security ecosystem for data security and privacy. In 2017 7th International conference on cloud computing, data science & engineering-confluence, pp. 288-292. IEEE (2017, January).
[15] Tahir, M., Sardaraz, M., Mehmood, Z., & Muhammad, S. CryptoGA: a cryptosystem based on genetic algorithm for cloud data security. Cluster Computing, 24(2), 739-752 (2021).
[16] Rahul, B., & Kuppusamy, K. Efficiency Analysis of Cryptographic Algorithms for Image Data Security at Cloud Environment. IETE Journal of Research, 1-12 (2021).
[17] Reshma, R. S., Anjusha, P. P., & Anisha, G. S. Implementing the Comparative Analysis of AES and DES Crypt Algorithm in Cloud Computing. In Computer Networks and Inventive Communication Technologies, pp. 325-332. Springer, Singapore (2022).
[18] Thabit, F., Can, O., Alhomdy, S., Al-Gaphari, G. H., & Jagtap, S. A Novel Effective Lightweight Homomorphic Cryptographic Algorithm for data security in cloud computing. International Journal of Intelligent Networks (2022).
[19] Ajmal, A., Ibrar, S., & Amin, R. Cloud computing platform: Performance analysis of prominent cryptographic algorithms. Concurrency and Computation: Practice and Experience, e6938.
[20] Murad, S. H., & Rahouma, K. H. Hybrid Cryptography for Cloud Security: Methodologies and Designs. In Digital Transformation Technology, pp. 129-140. Springer, Singapore (2022).
[21] Sohal, M., & Sharma, S. BDNA-A DNA inspired symmetric key cryptographic technique to secure cloud computing. Journal of King Saud University-Computer and Information Sciences, 34(1), 1417-1425 (2022).
[22] Bakro, M., Bisoy, S. K., Patel, A. K., & Naal, M. A. Hybrid Blockchain-Enabled Security in Cloud Storage Infrastructure Using ECC and AES Algorithms. In Blockchain based Internet of Things, pp. 139-170. Springer, Singapore (2022).
[23] Devmane, V., Lande, B. K., Joglekar, J., & Hiran, D. Preserving Data Security in Cloud Environment Using an Adaptive Homomorphic Blockchain Technique. Arabian Journal for Science and Engineering, 1-14 (2022).
[24] Khan, A. N., Fan, M. Y., Malik, A., & Memon, R. A. Learning from privacy preserved encrypted data on cloud through supervised and unsupervised machine learning. In 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), pp. 1-5. IEEE (2019, January).
[25] Bhamare, D., Salman, T., Samaka, M., Erbad, A., & Jain, R. Feasibility of supervised machine learning for cloud security. In 2016 International Conference on Information Science and Security (ICISS), pp. 1-5. IEEE (2016, December).
[26] Liu, Y., Sun, Y. L., Ryoo, J., Rizvi, S., & Vasilakos, A. V. A survey of security and privacy challenges in cloud computing: solutions and future directions. Journal of Computing Science and Engineering, 9(3), 119-133 (2015).
[27] Modi, C., Patel, D., Borisaniya, B., Patel, A., & Rajarajan, M. A survey on security issues and solutions at different layers of Cloud computing. The Journal of Supercomputing, 63(2), 561-592 (2013).
[28] Behl, A., & Behl, K. An analysis of cloud computing security issues. In 2012 world congress on information and communication technologies, pp. 109-114. IEEE (2012, October).
[29] Selamat, N., & Ali, F. Comparison of malware detection techniques using machine learning algorithm. Indones. J. Electr. Eng. Comput. Sci, 16, 435 (2019).
[30] Shamshirband, S., Fathi, M., Chronopoulos, A. T., Montieri, A., Palumbo, F., & Pescapè, A. Computational intelligence intrusion detection techniques in mobile cloud computing environments: Review, taxonomy, and open research issues. Journal of Information Security and Applications, 55, 102582 (2020).
[31] Elzamly, A., & Hussin, B. Classification of critical cloud computing security issues for banking organizations: A cloud Delphi study. International Journal of Grid and Distributed Computing, 9(8), 137-158 (2016).
[32] Guha, S., Yau, S. S., & Buduru, A. B. Attack detection in cloud infrastructures using artificial neural network with genetic feature selection. In 2016 IEEE 14th Intl Conf on Dependable, Autonomic and Secure Computing, 14th Intl Conf on Pervasive Intelligence and Computing, 2nd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech), pp. 414-419. IEEE (2016, August).
[33] Zamzam, M., Elshabrawy, T., & Ashour, M., Resource management using machine learning in mobile edge computing: a survey. In 2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS), pp. 112-117. IEEE (2019, December).
[34] Zardari, M. A., Jung, L. T., & Zakaria, N. K-NN classifier for data confidentiality in cloud computing. In 2014 International Conference on Computer and Information Sciences (ICCOINS), pp. 1-6. ieee (2014, June).
[35] Ali, A., Pasha, M. F., Ali, J., Fang, O. H., Masud, M., Jurcut, A. D., & Alzain, M. A. Deep Learning Based Homomorphic Secure 
Search-Able Encryption for Keyword Search in Blockchain Healthcare System: A Novel Approach to Cryptography. Sensors, 22(2), 528 (2022).
[36] Sajid, F., Hassan, M. A., Khan, A. A., Rizwan, M., Kryvinska, N., Vincent, K., & Khan, I. U. Secure and Efficient Data Storage Operations by Using Intelligent Classification Technique and RSA Algorithm in IoT-Based Cloud Computing. Scientific Programming, (2022).
[37] Anees, A., Hussain, I., Khokhar, U. M., Ahmed, F., & Shaukat, S. Machine Learning and Applied Cryptography. Security and Communication Networks, (2022).
[38] Oke, A., Kushwaha, A., Agrawal, M., Sahai, U., & Raman, R. A Novel Approach to Encrypt Data Using Deep Neural Networks. In Advances in Distributed Computing and Machine Learning, pp. 467-475. Springer, Singapore (2022).
[39] Hossain, M. J., Xu, C., Zhang, Y., Zhang, X., & Li, W. LAMA: A secure lattice-based authentication scheme for cloud storage against misbehaved private key generator. Journal of Ambient Intelligence and Humanized Computing, 1-17 (2022).
[40] Sharma, P., Jindal, R., & Borah, M. D. Blockchain-based cloud storage system with CP-ABE-based access control and revocation process. The Journal of Supercomputing, 1-29 (2022).
[41] Suresh, S. A., & Priyadarsini, R. J. Design of Maintaining Data Security on IoT Data Transferred Through IoT Gateway System to Cloud Storage. International Journal of Computer Networks and Applications, 9(1), 135-149 (2022).
[42] Tian, J., & Zhang, T. Secure and effective assured deletion scheme with orderly overwriting for cloud data. The Journal of Supercomputing, 1-29 (2022).
[43] Neela, K. L., & Kavitha, V. An Improved RSA Technique with Efficient Data Integrity Verification for Outsourcing Database in Cloud. Wireless Personal Communications, 1-18 (2022).
[44] Li, S., Xu, C., Zhang, Y., Du, Y., & Chen, K. Blockchain-Based Transparent Integrity Auditing and Encrypted Deduplication for Cloud Storage. IEEE Transactions on Services Computing (2022).
[45] Fu, Z., Wang, Y., Sun, X., & Zhang, X. Semantic and secure search over encrypted outsourcing cloud based on BERT. Frontiers of Computer Science, 16(2), 1-8 (2022).

Views: 150Downloads: 113Citations: 0