A Hybrid Approach to Secure Cloud Storage: Machine Learning and Blockchain Integration

Volume: 10 | Issue: 02 | Year 2024 | Subscription
International Journal of Distributed Computing and Technology
Received Date: 10/05/2024
Acceptance Date: 10/18/2024
Published On: 2024-11-18
First Page: 35
Last Page: 47

Journal Menu

https://doi.org/10.37628/ijdct.v10i02.12019

By: Harshvardhan Chunawala and Pratik Kumar Chunawala.

Abstract

With the rapid adoption of cloud computing across industries, securing cloud storage has become a paramount concern for organizations that rely on cloud-based systems to manage sensitive data. Traditional security approaches often struggle with the complex and evolving nature of cyber threats, leading to vulnerabilities that malicious actors can exploit. In response to these challenges, this paper proposes a hybrid approach that integrates machine learning and blockchain technologies to create a robust, secure cloud storage solution. The suggested system employs machine learning algorithms to identify potential threats and assess anomalies in real-time. By constantly monitoring cloud storage environments, these models can spot and predict potential security issues, enabling proactive measures to reduce risks. At the same time, blockchain technology is applied to maintain data integrity, ensure transparency, and provide decentralized access control. The immutable nature of blockchain records provides a tamper-proof mechanism for storing access logs and transaction histories, enhancing the overall trustworthiness of the cloud storage system. This hybrid approach addresses key security challenges by combining the strengths of machine learning and blockchain. Machine learning’s capacity to adapt and learn from emerging threats works hand in hand with blockchain’s secure and transparent approach to data management. By integrating these technologies, the system not only protects against unauthorized access and data breaches but also improves the scalability and efficiency of cloud operations. Extensive testing and evaluation of the proposed framework demonstrate significant improvements in threat detection accuracy, data integrity assurance, and access control over traditional security methods. The results suggest that this hybrid approach is a viable solution for enhancing cloud storage security in various applications, ranging from enterprise data management to IoT ecosystems. This study advances cloud security technologies by providing a scalable, robust, and future-ready solution for protecting cloud environment.

Keywords: Cloud storage security, machine learning, blockchain, hybrid approach, data integrity, threat detection, access control

Loading

Citation:

How to cite this article: Harshvardhan Chunawala and Pratik Kumar Chunawala A Hybrid Approach to Secure Cloud Storage: Machine Learning and Blockchain Integration. International Journal of Distributed Computing and Technology. 2024; 10(02): 35-47p.

How to cite this URL: Harshvardhan Chunawala and Pratik Kumar Chunawala, A Hybrid Approach to Secure Cloud Storage: Machine Learning and Blockchain Integration. International Journal of Distributed Computing and Technology. 2024; 10(02): 35-47p. Available from:https://journalspub.com/publication/ijdct/article=12019

Refrences:

  1. Xie G, Zeng G, Li R, Li K. Quantitative Fault-Tolerance for Reliable Workflows on Heterogeneous IaaS Clouds. IEEE Transact Cloud Compu. 2020 Oct 1;8(04):1223–36.
  2. Zheng Z, Xie S, Dai H, Chen X, Wang H. An overview of blockchain technology: Architecture, consensus, and future trends. In: 2017 IEEE International Congress on Big Data (BigData congress). 2017 Jun 25:557–564. IEEE.
  3. Singh H, Mallaiah R, Yadav G, Verma N, Sawhney A, Brahmachari SK. iCHRCloud: web & mobile based child health imprints for smart healthcare. J Med Sys. 2018 Jan;42(1):14.
  4. Itani W, Kayssi A, Chehab A. Privacy as a service: Privacy-aware data storage and processing in cloud computing architectures. In: Eighth IEEE International Conference on Dependable, Autonomic and Secure Computing. 2009 Dec. 12:71116.
  5. Nakamoto S. Bitcoin. A peer-to-peer electronic cash system. 2008;21260.
  6. Tian F. An agri-food supply chain traceability system for China based on RFID & blockchain technology. In: 13th international conference on service systems and service management (ICSSSM) 2016 Jun 24:1–6.
  7. Wang F, Xu L, Wang H, Chen Z. Identity-based non-repudiable dynamic provable data possession in cloud storage. Compu & Elec Eng. 2018 Jul 1;69:521–33.
  8. Zhang W, Tian D. Find Evasion: An effective environment-sensitive malware detection system for the cloud. In: Digital Forensics and Cyber Crime: 9th International Conference, ICDF2C 2017, Prague, Czech Republic, October 9–11, 2017, Proceedings 2018 Jan 4;216:3. Springer.
  9. Dorri A, Steger M, Kanhere SS, Jurdak R. Blockchain: A distributed solution to automotive security and privacy. IEEE communications magazine. 2017 Dec 13;55(12):119–25.
  10. Raj A, Jain N, Chauhan SS. Mapping of Security Issues and Concerns in Cloud Computing with Compromised Security Attributes. In: Cybersecurity in Emerging Digital Era: First International Conference, ICCEDE 2020, Greater Noida, India, October 9–10, 2020, Revised Selected Papers 1. Springer International Publishing. 2021. pp. 24–40.
  11. Hur J, Noh DK. Attribute-based access control with efficient revocation in data outsourcing systems. IEEE Transactions on Parallel and Distributed Systems. 2010 Nov 11;22(7):1214–21.
  12. Lu J, Shen J, Vijayakumar P, Gupta BB. Blockchain-based secure data storage protocol for sensors in the industrial internet of things. IEEE Transactions on Industrial Informatics. 2021 Sep 14;18(8):5422–31.
  13. Kaur J, Singh G. A blockchain-based machine learning intrusion detection system for internet of things. In: Principles and Practice of Blockchains. Cham: Springer International Publishing. 2022 Jul 4 (pp. 119–134).
  14. Swarnkar SK, Dewangan L, Dewangan O, Prajapati TM, Rabbi F. AI-enabled Crop Health Monitoring and Nutrient Management in Smart Agriculture. In: 2023 6th International Conference on Contemporary Computing and Informatics. 2023 Sep 14;6:2679–2683). IEEE.
  15. Liu B, Yu XL, Chen S, Xu X, Zhu L. Blockchain based data integrity service framework for IoT data. In: 2017 IEEE international conference on web services (ICWS) 2017 Jun 25:468–475. IEEE.
  16. Cha J, Singh SK, Kim TW, Park JH. Blockchain-empowered cloud architecture based on secret sharing for smart city. Journal of Information Security and Applications. 2021 Mar 1;57:102686.
  17. Baldassarre MT, Caivano D, Dimauro G, Gentile E, Visaggio G. Cloud computing for education: a systematic mapping study. IEEE transactions on education. 2018 Feb 6;61(3):234–44.
  18. Wang Q, Wang C, Li J, Ren K, Lou W. Enabling public verifiability and data dynamics for storage security in cloud computing. In: Computer Security–ESORICS 2009: 14th European Symposium on Research in Computer Security, Saint-Malo, France, September 21–23, 2009. Berlin Heidelberg: Springer. Proceedings 14. 2009. pp. 355–370.
  19. Monrat AA, Schelén O, Andersson K. A survey of blockchain from the perspectives of applications, challenges, and opportunities. IEEE Access. 2019 Aug 19;7:117134–51.
  20. Swarnkar SK, Ambhaikar A, Swarnkar VK, Sinha U. Optimized Convolution Neural Network (OCNN) for Voice-Based Sign Language Recognition: Optimization and Regularization. In: Information and Communication Technology for Competitive Strategies (ICTCS 2020) ICT: Applications and Social Interfaces 2022 (pp. 633–639). Springer Singapore.
  21. Ali M, Dhamotharan R, Khan E, Khan SU, Vasilakos AV, Li K, Zomaya AY. SeDaSC: secure data sharing in clouds. IEEE Systems Journal. 2015 Jan 13;11(2):395–404.
  22. Yu C, Zhang L, Zhao W, Zhang S. A blockchain-based service composition architecture in cloud manufacturing. International Journal of Computer Integrated Manufacturing. 2020 Jul 2;33(7):701–15
  23. Sahoo JP, Tripathy AK, Mohanty M, Li KC, Nayak AK. Advances in Distributed Computing and Machine Learning. Proceedings of ICADCML 2021. Springer Singapore; 2022
  24. Cha J, Singh SK, Kim TW, Park JH. Blockchain-empowered cloud architecture based on secret sharing for smart city. Journal of Information Security and Applications. 2021 Mar 1;57:102686.
  25. Swarnkar DM, Ambhaikar A. Improved convolutional neural network based sign language recognition. International Journal of Advanced Science and Technology. 2019 Aug;27(1):302–17.
  26. Fan Y, Liu S, Tan G, Qiao F. Fine-grained access control based on trusted execution environment. Future Generation Computer Systems. 2020 Aug 1;109:551–61.
  27. Ghaffari F, Gharaee H, Forouzandehdoust MR. Security considerations and requirements for Cloud computing. In: 2016 8th International Symposium on Telecommunications (IST). 2016 Sep 27: 105–110.
  28. Dhaygude AD, Varma RA, Yerpude P, Swarnkar SK, Jindal RK, Rabbi F. Deep Learning Approaches for Feature Extraction in Big Data Analytics. In:2023 10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) 2023 Dec 1;10:964–969.
  29. Adi S. How to share a secret. Commun. ACM. 1979;22:612–3.
  30. Rivest RL, Shamir A, Adleman L. A method for obtaining digital signatures and public-key cryptosystems. Communications of the ACM. 1983 Jan 1;26(1):96–9.
  31. Wang H, Zhang J. Blockchain based data integrity verification for large-scale IoT data. IEEE Access. 2019 Nov 11;7:164996–5006.
  32. Du X, Zhou Z, Zhang Y, Rahman T. Energy-efficient sensory data gatheri.ng based on compressed sensing in IoT networks. Journal of Cloud Computing. 2020 Dec;9:1–6.
  33. Devarajan HR, Balasubramanian S, Swarnkar SK, Kumar P, Jallepalli VR. Deep Learning for Automated Detection of Lung Cancer from Medical Imaging Data. In: 2023 International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI) 2023 Dec 29;1:1–5.
  34. Gaikwad, V. S., Shivaji Deore, S., Poddar, G. M., V. Patil, R., Sandeep Hirolikar, D., Pravin Borawake, M., & Swarnkar, S. K. (2024). Unveiling Market Dynamics through Machine Learning: Strategic Insights and Analysis. International Journal of Intelligent Systems and Applications in Engineering, 12(14s):388–39.
  35. Chhabra GS, Guru A, Rajput BJ, Dewangan L, Swarnkar SK. Multimodal Neuroimaging for Early Alzheimer’s detection: A Deep Learning Approach. In: 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT) 2023 Jul 6:1–5.

https://doi.org/10.37628/ijdct.v10i02.12019
Previous Article
Next Article

Powered by
►
Necessary cookies enable essential site features like secure log-ins and consent preference adjustments. They do not store personal data.
None
►
Functional cookies support features like content sharing on social media, collecting feedback, and enabling third-party tools.
None
►
Analytical cookies track visitor interactions, providing insights on metrics like visitor count, bounce rate, and traffic sources.
None
►
Advertisement cookies deliver personalized ads based on your previous visits and analyze the effectiveness of ad campaigns.
None
Powered by