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All Journal International Journal of Multidisciplinary Sciences and Arts BULLET : Jurnal Multidisiplin Ilmu
Md Farhad Kabir
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Humanities Computer Science & IT Economics, Econometrics & Finance Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing

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ENHANCING DATA PRIVACY AND SECURITY IN MULTI CLOUD ENVIRONMENTS Md Emran Hossain; Md Farhad Kabir; Abdullah Al Noman; Nipa Akter; Zakir Hossain
BULLET : Jurnal Multidisiplin Ilmu Vol. 1 No. 05 (2022): BULLET : Jurnal Multidisiplin Ilmu
Publisher : CV. Multi Kreasi Media

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Abstract

In this study, we present and realize a solution for contributing to the provision of data security and data privacy in a hybrid configuration based Multi Cloud environment. This method combines prevention of independent cloud security attacks and server failures through a Byzantine fault tolerance protocol, a data encoding and decoding mechanism using the Dusky architecture to improve reliability and confidentiality; and Shamir's secret sharing scheme to guarantee data trustworthiness and privacy during storage at the cost of a minor performance implication. They compared the security and privacy of their hybrid approach with well-known protocols such as SAML with proxy encryption and Kerberos, showing the benefits in terms of memory footprint, encryption/decryption time and totaltimetoauthenticate. The experimental results show that our hybrid scheme provides considerable improvements with regard to encryption\\/decryption time, memory consumption and average precision.
Block chain-Based Solutions for Improved Cloud Data Integrity and Security Md Tanvir Rahman Tarafder; Arafath Bin Mohiuddin; Nisher Ahmed; Md Abu Shihab; Md Farhad Kabir
BULLET : Jurnal Multidisiplin Ilmu Vol. 1 No. 04 (2022): BULLET : Jurnal Multidisiplin Ilmu ( Agustus-September)
Publisher : CV. Multi Kreasi Media

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The evolution of cloud computing, however, has not been all smooth sailing, and it still struggles with data security and trusted computing to this day. Alternative solutions like data integrity tests and secure multi-party computation is often accompanied by computational complexity and scalability concerns. Block chain technology has been developed as an abstract setting of decentralized distributed computing, and applied to secure storage and computation in various cloud environments. In response, we propose a distributed virtual machine agent model that uses the mobile agent technology to create a cooperative and multi-tenant environment for the verification of trust in the data. This model assures the data are stored and monitored reliable way and provides a verification method that is the base of a Block chain based integrity protection mechanism. Using this model, we create a Block chain model that utilizes Merkle hash trees in order to create unique file hashes. Smart contracts monitor changes to this data and will notify users of any tampering. Additionally, in a block-and-response manner, it provides a strong cloud data integrity verification solution.
The Role of AI and Machine Learning in Optimizing Cloud Resource Allocation Abdullah Al Noman; Zakir Hossain; Md Abu Shihab; Nipa Akter; Nur Nahar Rimi; Md Farhad Kabir
International Journal of Multidisciplinary Sciences and Arts Vol. 2 No. 1 (2023): International Journal of Multidisciplinary Sciences and Arts, Article January 2
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/ijmdsa.v1i2.2190

Abstract

Resource allocation inside the cloud infrastructure is an essential requirement for better performance and cost-effectiveness. With workloads growing so much more complex and dynamic, we now need automated solutions that allow us to move past tedious, manual resource management. AI provides a unique paradigm for optimizing resource allocation in such complex and dynamic environments. In this paper, we have compared various AI techniques such as machine learning, evolutionary algorithms and deep reinforcement learning for the problem of resource allocation in cloud infrastructure. We analyze their effectiveness for dynamic resource provisioning to satisfy performance objectives while reducing operational expenditure. We offer an in-depth performance comparison using real-world datasets and simulations to showcase the merits and drawbacks of each. Implications: We believe that our findings can offer guidelines to cloud providers, researchers, and practitioners who are interested in making proactive improvements on managing cloud infrastructures using intelligent resource allocation techniques.
Co-Authors Abdullah Al Noman Arafath Bin Mohiuddin Md Abu Shihab Md Emran Hossain Md Tanvir Rahman Tarafder Nipa Akter Nisher Ahmed Nur Nahar Rimi Zakir Hossain